基于视觉感知和相关反馈机制的图像检索算法研究
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摘要
随着数字图像数量的飞速增长,如何高效、快速地从海量图像数据中检索出所需要的信息已成为当前图像应用领域的一个重要问题。图像内容可以分为两类,即视觉内容与语义内容。视觉内容也就是通常所讲的图像的物理表示,如：纹理、颜色、形状、方向等；语义内容是指图像的信息,如：主题、场景、人物等。为了检索图像,首先要对图像内容进行理解,并将其内容形式化表示。由于图像内容的复杂性和人类认知的主观性,对图像进行正确的理解和表示是一项很困难的工作。
本文主要围绕基于内容图像检索(Content-based image retrieval, CBIR)中的图像特征提取及克服“语义鸿沟”等相关方面的技术展开研究,系统讨论了图像信息表示、特征提取和相关反馈技术。研究的内容属于目前图像处理和信息检索领域的研究重点,具体研究包括：
为充分利用视觉注意模型的显著性信息和Contourlet变换良好的稀疏性及能准确地捕获图像中边缘信息的特性,提出一种基于视觉注意及Contourlet变换的图像检索算法。首先对颜色、亮度和方向特征采用Itti视觉注意模型获取特征图,采用图像的Contourlet多尺度分析方法获取图像的子带,然后,在获取特征图和Contourlet方向子带的基础上,运用局部二值模式傅里叶直方图(Local Binary Pattern Histogram Fourier, LBP -HF),有效地提取每个特征图中的综合显著信息,采用每个方向子带的一阶和二阶矩抽取纹理信息,通过将显著信息和纹理信息有机结合,实现多特征融合的图像检索。
由于视觉注意模型能够在注意机制的驱动下,依据用户的感知将图像中的关键信息突出出来,同时抑制那些非主要信息,因此,在研究Itti视觉注意模型的基础上,通过构造不同的初级视觉特征,提出了两种基于视觉注意的特征抽取算法。算法-在充分考虑纹理特征与视觉感知关系的基础上,构造一个粗糙度图,用作视觉注意模型的一个初级视觉特征。通过该改进视觉注意模型得到50个视觉特征图,然后分别对每个视觉特征图采用局部二值模式傅里叶直方图(LBP-HF)方法抽取其分布信息,从而获得每幅图像的高维特征,最后利用局部保持投影(Locality Preserving Projects,LPP)方法进行维数约简,获取低维特征,用于图像检索。算法二,由于采用高对比度获得显著性图是一种合理的仿生途径,为选择带有最多细节、拥有最大对比度且灰度值分布最广的频谱分量,用主分量图作为亮度初级视觉特征,并引入具有丰富边缘、纹理和形状信息的梯度图作为视觉注意模型的一个初级视觉特征,改进Itti视觉注意模型。基于该改进视觉注意模型得到50个视觉特征图,对这50个视觉特征图运用方法一相同的过程,获取低维特征,进行图像检索。
将先验标签信息引入到学习过程中,可以从两个方面来考虑,一是运用适当的距离测度学习,二是采用有监督特征映射子空间学习。
基于不同的距离测度学习方式,本文提出了两种相关反馈算法。基本思路是使用有鉴别信息的距离测度来代替欧氏距离测度,将先验鉴别信息引入到k-近邻块中,增大不同类间的间隔,提高流形学习的泛化能力；同时利用无标签图像点的分布信息半监督学习各图像点间的相关性,用图策略来建立图像库中各图像点间的相关性模型,当有标签图像点不足时,依靠无标签图像点的分布信息能够尽可能准确地挖掘出图像间所潜在的语义信息来辅助检索的实现。
运用有监督特征映射子空间学习,发现高维数据中有意义的低维嵌入空间也是引入已知标签信息,构建相关反馈机制的一种有效途径。基于此,本文提出了两种相关反馈算法：算法一,利用每次反馈用户标注的先验图像点标签信息构建类内最近邻图和类间最近邻图,刻画图像数据库的几何和鉴别结构,在整体图的约束下,寻找一个最优投影,在这个低维投影空间中,采用线性近邻标签传递算法进行标签标注传递；算法二,充分利用每次反馈用户标注的图像点的标签信息和无标签图像点的空间几何分布信息,半监督学习各图像点间的相关性,克服已知标签较少时对线性鉴别分析(Linear Discriminant Analysis, LDA)有效性的影响,将线性签别分析(LDA)与线性近邻传递算法相融合,获取鉴别嵌入子空间,并采用线性近邻标签传递算法完成标签信息标注,进而实现相关反馈的图像检索。
With the substantial increasing number of digital images, it has become an important issue in the image application field to retrieve required information from massive image data efficiently and rapidly. Image contents can be classified into two categories:visual content and semantic content. The former is usually called "image out of physical representation", such as texture, color, shape, direction, etc; the latter refers to image information, such as theme, scene, characters, etc. To retrieve image from data, the first step is to understand the image and represent its information correctly. However, it is rather difficult to realize due to the complexity of image content and subjectivity of human being's cognition.
This paper makes a research on how to extract features while overcoming semantic wide gap on content-based image retrieval (CBIR). It discusses systematically on image information representation, feature extraction and relevant feedback technologies. Dealing with a key research point in the image processing and information retrieval field, the contents of the thesis are as follows:
In order to make full use of saliency information of visual attention model, good sparsity of Contourlet transform and its trait of being able to accurately capture the images of edge information, an algorithm based on visual attention and Contourlet transform was proposed:first, to obtain feature map to the color, brightness and direction feature through Itti visual attention model and to acquire image direction sub-bands through Contourlet multiscale analysis; and then to extract saliency information from each feature map efficiently through Local Binary Pattern Histogram Fourier, and to extract texture information through the first and second order moment in each direction sub-band. When all these are combined, image retrieval based on multi-features fusion will be realized.
Driven by the attention mechanism, the visual attention model will highlighten the key information and suppress those non-native main information according to user's sensation. Thus, after researching Itti visual attention model and building different primary visual features, the author proposes two feature extraction algorithms on the basis of visual attention. Algorithm 1, to form a roughness map based on the relationship between texture characteristics and visual perception, and then taking it as a primary visual feature in visual attention model. By improving the visual attention model, we can get 50 visual feature maps and extract their distribution information respectively by method of LBP-HF and get the large-dimensional characteristics of each image. At last the dimension reduction of these images by Locality Preserving projects (LPP) can help us get low dimension feature for image retrieval. Algorithm 2, as the high contrast acquired distinctiveness map is a reasonable biomimetic way, to choose the spectrum component containing most details and the largest contrast and grey value of the most extensive distribution, we use principal component map as brightness primary visual feature. And we use the gradient map with rich edge, texture and shape information as the primary visual feature of the visual attention model. With the improved Itti visual attention model, we can obtain 50 visual feature maps, the low dimension feature of which can be used for image retrieval by the same processes of algorithm 1.
A priori label information is introduced into the learning process, we shall take two aspects into consideration:one is to use a proper distance measure learning, and the other is to employ supervised feature mapping subspace learning.
This paper, based on different distance measure learning methods, proposes two relevant feedback algorithms. By replacing the Euclidean distance measure with the identifiable information distance measure, putting the priori identification information into the k -neighbor block and increasing the interval of different classes to improve the generalization ability of manifold learning; at the same time, applying the distribution information of no label image point to semi-supervising learning the relation of each image point, and establishing the relation model between each image point in image database by graph strategies. Whenever there is defect in label image points, we can rely on the distribution information of no label image points to dig the potential semantic information of the figure inside structure as accurately as possible to assist the retrieval realization.
Feature mapping subspace learning is an efficient method to find the meaningful low dimensional embedded space in high-dimension observation data, which introduces label information and construct the relevant feedback mechanisms. Therefrom, this paper proposes two algorithms. Algorithm 1 is mainly to construct the nearest chart of inside class and the nearest neighbor diagram between classes which uses a priori feedback image point label information of each user noted, to depict the geometry and identifiable structure of image database, so as to find an optimal projection under the constraint of the whole figure. Under this low-dimensional projection space, to transfer label information by linear neighborhood propagation(LNP) algorithm; Algorithm 2 is mainly to makes use of label information which is noted by user and space geometry distribution information of no label image points to semi-supervised learning the relation of image points and to overcome the influence of LAD effectiveness when the known label is fewer; to combine the LDA with LNP algorithm, to get identifiable embedded subspace, and to complete the note of label information using LNP algorithm, and finally to realize the image retrieval of relevant feedback.
本文主要围绕基于内容图像检索(Content-based image retrieval, CBIR)中的图像特征提取及克服“语义鸿沟”等相关方面的技术展开研究,系统讨论了图像信息表示、特征提取和相关反馈技术。研究的内容属于目前图像处理和信息检索领域的研究重点,具体研究包括：
为充分利用视觉注意模型的显著性信息和Contourlet变换良好的稀疏性及能准确地捕获图像中边缘信息的特性,提出一种基于视觉注意及Contourlet变换的图像检索算法。首先对颜色、亮度和方向特征采用Itti视觉注意模型获取特征图,采用图像的Contourlet多尺度分析方法获取图像的子带,然后,在获取特征图和Contourlet方向子带的基础上,运用局部二值模式傅里叶直方图(Local Binary Pattern Histogram Fourier, LBP -HF),有效地提取每个特征图中的综合显著信息,采用每个方向子带的一阶和二阶矩抽取纹理信息,通过将显著信息和纹理信息有机结合,实现多特征融合的图像检索。
由于视觉注意模型能够在注意机制的驱动下,依据用户的感知将图像中的关键信息突出出来,同时抑制那些非主要信息,因此,在研究Itti视觉注意模型的基础上,通过构造不同的初级视觉特征,提出了两种基于视觉注意的特征抽取算法。算法-在充分考虑纹理特征与视觉感知关系的基础上,构造一个粗糙度图,用作视觉注意模型的一个初级视觉特征。通过该改进视觉注意模型得到50个视觉特征图,然后分别对每个视觉特征图采用局部二值模式傅里叶直方图(LBP-HF)方法抽取其分布信息,从而获得每幅图像的高维特征,最后利用局部保持投影(Locality Preserving Projects,LPP)方法进行维数约简,获取低维特征,用于图像检索。算法二,由于采用高对比度获得显著性图是一种合理的仿生途径,为选择带有最多细节、拥有最大对比度且灰度值分布最广的频谱分量,用主分量图作为亮度初级视觉特征,并引入具有丰富边缘、纹理和形状信息的梯度图作为视觉注意模型的一个初级视觉特征,改进Itti视觉注意模型。基于该改进视觉注意模型得到50个视觉特征图,对这50个视觉特征图运用方法一相同的过程,获取低维特征,进行图像检索。
将先验标签信息引入到学习过程中,可以从两个方面来考虑,一是运用适当的距离测度学习,二是采用有监督特征映射子空间学习。
基于不同的距离测度学习方式,本文提出了两种相关反馈算法。基本思路是使用有鉴别信息的距离测度来代替欧氏距离测度,将先验鉴别信息引入到k-近邻块中,增大不同类间的间隔,提高流形学习的泛化能力；同时利用无标签图像点的分布信息半监督学习各图像点间的相关性,用图策略来建立图像库中各图像点间的相关性模型,当有标签图像点不足时,依靠无标签图像点的分布信息能够尽可能准确地挖掘出图像间所潜在的语义信息来辅助检索的实现。
运用有监督特征映射子空间学习,发现高维数据中有意义的低维嵌入空间也是引入已知标签信息,构建相关反馈机制的一种有效途径。基于此,本文提出了两种相关反馈算法：算法一,利用每次反馈用户标注的先验图像点标签信息构建类内最近邻图和类间最近邻图,刻画图像数据库的几何和鉴别结构,在整体图的约束下,寻找一个最优投影,在这个低维投影空间中,采用线性近邻标签传递算法进行标签标注传递；算法二,充分利用每次反馈用户标注的图像点的标签信息和无标签图像点的空间几何分布信息,半监督学习各图像点间的相关性,克服已知标签较少时对线性鉴别分析(Linear Discriminant Analysis, LDA)有效性的影响,将线性签别分析(LDA)与线性近邻传递算法相融合,获取鉴别嵌入子空间,并采用线性近邻标签传递算法完成标签信息标注,进而实现相关反馈的图像检索。
With the substantial increasing number of digital images, it has become an important issue in the image application field to retrieve required information from massive image data efficiently and rapidly. Image contents can be classified into two categories:visual content and semantic content. The former is usually called "image out of physical representation", such as texture, color, shape, direction, etc; the latter refers to image information, such as theme, scene, characters, etc. To retrieve image from data, the first step is to understand the image and represent its information correctly. However, it is rather difficult to realize due to the complexity of image content and subjectivity of human being's cognition.
This paper makes a research on how to extract features while overcoming semantic wide gap on content-based image retrieval (CBIR). It discusses systematically on image information representation, feature extraction and relevant feedback technologies. Dealing with a key research point in the image processing and information retrieval field, the contents of the thesis are as follows:
In order to make full use of saliency information of visual attention model, good sparsity of Contourlet transform and its trait of being able to accurately capture the images of edge information, an algorithm based on visual attention and Contourlet transform was proposed:first, to obtain feature map to the color, brightness and direction feature through Itti visual attention model and to acquire image direction sub-bands through Contourlet multiscale analysis; and then to extract saliency information from each feature map efficiently through Local Binary Pattern Histogram Fourier, and to extract texture information through the first and second order moment in each direction sub-band. When all these are combined, image retrieval based on multi-features fusion will be realized.
Driven by the attention mechanism, the visual attention model will highlighten the key information and suppress those non-native main information according to user's sensation. Thus, after researching Itti visual attention model and building different primary visual features, the author proposes two feature extraction algorithms on the basis of visual attention. Algorithm 1, to form a roughness map based on the relationship between texture characteristics and visual perception, and then taking it as a primary visual feature in visual attention model. By improving the visual attention model, we can get 50 visual feature maps and extract their distribution information respectively by method of LBP-HF and get the large-dimensional characteristics of each image. At last the dimension reduction of these images by Locality Preserving projects (LPP) can help us get low dimension feature for image retrieval. Algorithm 2, as the high contrast acquired distinctiveness map is a reasonable biomimetic way, to choose the spectrum component containing most details and the largest contrast and grey value of the most extensive distribution, we use principal component map as brightness primary visual feature. And we use the gradient map with rich edge, texture and shape information as the primary visual feature of the visual attention model. With the improved Itti visual attention model, we can obtain 50 visual feature maps, the low dimension feature of which can be used for image retrieval by the same processes of algorithm 1.
A priori label information is introduced into the learning process, we shall take two aspects into consideration:one is to use a proper distance measure learning, and the other is to employ supervised feature mapping subspace learning.
This paper, based on different distance measure learning methods, proposes two relevant feedback algorithms. By replacing the Euclidean distance measure with the identifiable information distance measure, putting the priori identification information into the k -neighbor block and increasing the interval of different classes to improve the generalization ability of manifold learning; at the same time, applying the distribution information of no label image point to semi-supervising learning the relation of each image point, and establishing the relation model between each image point in image database by graph strategies. Whenever there is defect in label image points, we can rely on the distribution information of no label image points to dig the potential semantic information of the figure inside structure as accurately as possible to assist the retrieval realization.
Feature mapping subspace learning is an efficient method to find the meaningful low dimensional embedded space in high-dimension observation data, which introduces label information and construct the relevant feedback mechanisms. Therefrom, this paper proposes two algorithms. Algorithm 1 is mainly to construct the nearest chart of inside class and the nearest neighbor diagram between classes which uses a priori feedback image point label information of each user noted, to depict the geometry and identifiable structure of image database, so as to find an optimal projection under the constraint of the whole figure. Under this low-dimensional projection space, to transfer label information by linear neighborhood propagation(LNP) algorithm; Algorithm 2 is mainly to makes use of label information which is noted by user and space geometry distribution information of no label image points to semi-supervised learning the relation of image points and to overcome the influence of LAD effectiveness when the known label is fewer; to combine the LDA with LNP algorithm, to get identifiable embedded subspace, and to complete the note of label information using LNP algorithm, and finally to realize the image retrieval of relevant feedback.
引文
[1]卢汉清.图像视频信号的浏览与检索.中国图象图形学报,2000,5(B)(1)：5-9.
[2]Li J, Wang J Z, Wiedherold G. IRM:integrated region matching for image retrieval. Proceeding of ACM Multimedia,2000,147-156.
[3]Wang J Z, Li J, Wiederhold G. SIMPLIcity:semantics-sensitive integrated matching for Picture libraries.IEEE Trans. on PAMI,2001,23(9):1-17.
[4]Cllad C, Serge B, Hayit G, et al. Blockworld:image segmentation using expectation-maximization and its application to image querying. IEEE Trans on PAMI, 2002,24(8):1026-1038.
[5]袁昕,朱淼良.基于主色匹配的图像检索系统.计算机辅助设计与图形学学报,2000,12(12)：917-921.
[6]汪祖媛,梁栋,李斌.基于树状小波分解的纹理图像检索.中国图象图形学报,2001,6A(11)：1065-1069.
[7]何清法,李国杰.综合分块主色和相关反馈技术的图像检索方法.计算机辅助设计与图形学学报,2001,13(10)：912-917.
[8]王涛,胡事民,孙家广.基于颜色-空间的图像检索.软件学报,2002,13(10)：2031-2036.
[9]Lee H Y, Lee H K, Ha Y H. Spatial color descriptor for image retrieval and video segmentation. IEEE Trans. On Multimedia,2003,5(3):358-367.
[10]黄元元,郭丽,杨静宇.基于目标区域颜色与纹理特征的图像检索.南京理工大学学报,2003,27(3)：286-289.
[11]Qiu G P, Lam K M. Frequency layered color indexing for content-based image retrieval. IEEE Transactions on Image Processing,2003,12(1):102-113.
[12]洪安详.基于内容的图像检索若干论题研究.博士论文,浙江大学,2003.
[13]刘进,张天序.图像不变矩的推广.计算机学报,2004,27(5)：668-674.
[14]Ashley J, Barber R, Flickner M, et al. Automatic and semi-automatic methods for image annotation and retrieval in QBIC. SPIE 2420,1995, pp.24-35.
[15]Huang T S, Mehrotra S, Ranlchandran K. Multimedia analysis and retrieval system (MARS) project. In Proc. of 33dr Annual Clinic on Library Application of Data Processing Digital Image Access and Retrieval,1996.
[16]庄越挺,潘云鹤.基于内容的图像检索综述.模式识别与人工智能,1999,12(2)：170-177.
[17]Weber R, Schek H J, Blott S. A quantitative analysis and performance study forsimilarity-serch methods in high-dimensional spaces. Proc.24 Int. Conf. VLDB. New York:IEEE,1998,194-205.
[18]Eakins J P, Graham M E. Content-based image retrieval:a report to the JISC technology applications programme. Institute for image data researeh Newcastle-upon-tyne,1999,1-63.
[19]Rui Y, Huang T, Chang F. Image retrieval:Current techniques, promising directions, and open issues. Visual Comm. Image Represent,1999,10(1):39-62.
[20]李国辉,胡晓峰.基于内容的检索.计算机世界报,1998(18)78-85.
[21]Flickner M, Sawhney H, Niblack W, et al. Query by image and video content:the QBIC system. IEEE Computer,1995,28(9):23-32.
[22]Smith J R, Chang Shih-fu. VisualSEEK:a fully automated content-based image query system. [1996-07-23/2010-12-10]. http://citeseer.ist.psu.edu/smith96visualseek.htm 1.
[23]Bach J R, Fuller C, Gupta A, et al. The virage image search engine:an open framework for image management. Proc. SPIE Storage and Retrieval for Image and Video Database,1996,2670:76-87.
[24]Pentland A, Picard R W, Sclaroff S. Photobook:Content-Based manipulation of image databases. International Journal of Computer Vision,1996,18(3):233-254.
[25]Ogle V E, Stonebraker M. Chabot:retrieval from a relational database of images. IEEE Computer,1995,28(9):40-48.
[26]Chang S F, Chen W, Sundaram H. Semantic visual templates:Linking visual features to semantics. Proc. IEEE ICIP. Chicago:IL,1998,3:531-535.
[27]Lu Y, Hu C, Zhou X, et al. A unified framework for semantics and feature based relevance feedback in image retrieval systems. Proc of ACM Multimedia conference,2000, 31-37.
[28]Zhu X Q, Zhang H J. New query refinement and semantics integrated image retrieval system with semiautomatic annotation scheme. Journal of Electronic Imaging. 2001,10(4):850-860.
[29]Han J W, Guo L. A new image retrieval model supporting query by semantics and example. IEEE Conference on Image Processing. New York:IEEE,2002,3:953-956.
[30]Ynag J, Li Q, Zhuang Y T. Image retrieval and relevance feedback using peer indexing. Proc. IEEE conference on ICME,2002,409-412.
[31]Lim J H, Jin J S. Image indexing and retrieval using visual keyword histograms. Proe. IEEE conference on ICME,2002,1:213-216.
[32]Zhao R, Grosky W I. Negotiating the semantic gap:from feature maps to semantic landscapes. Pattern Recognition,2002,35(3):593-600.
[33]Zhou X S, Huang T S. Unifying keywords and visual contents in image retrieval. IEEE Multimedia,2002,9(2):23-33.
[34]Zhao R, Grosky W I. Narrowing the semantic gap-improved text-based web document retrieval using visual features. IEEE Trans. on Multimedia,2002,4(2):189-200.
[35]Tsotsos J K. Analyzing vision at the complexity level. Behavioral and Brain Sciences,1990,13:423-445.
[36]张菁,沈兰荪,David D F,基于视觉感知的图像检索的研究.电子学报,2008,36(3)：494-499.
[37]沈兰荪,张菁,李晓光.图像检索与压缩域处理技术的研究.北京：人民邮电出版社,2008：113-114.
[38]Treisman A M, Gelade G. A feature-integration theory of attention. Cognitive Psychology,1980,12(1):97-136.
[39]Koch C, Ullman S. Shifts in Selective Visual Attention:Towards the Underlying Neural Circuitry. Human Neurobiology,1985,4:219-227.
[40]Itti L, Koch C, Niebur E. A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998, 20(11):1254-1259.
[41]Itti L, Koch C. A Comparison of Feature Combination Strategies for Saliency-based Visual Attention Systems. In:Proc.SPIE Human Vision and Electronic Imaging IV(HVEI'99), San Jose, CA, Bellingham, WA:SPIE Press, Jan 1999, Vol.3644,pp.472-82.
[42]L.Itti. Models of Bottom-up and Top-down Visual Attention. PhD Thesis,2000.
[43]Itti L, Koch C. Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging,2001,10(1):161-169
[44]Itti L, Koch C. Computational Modeling of Visual Attention. Nature Reviews Neuroscience,2001,2(3):193-203.
[45]Sun Y, Fisher R. Object-based Visual Attention for Computer Vision. Artificial Intelligence,2003,146(1):77-123.
[46]Satoh S, Miyake S. A Model of Overt Visual Attention Based on Scale-Space Theory. Systems and Computers in Japan,2004,35(10):1-13.
[47]张鹏,王润生.基于视点转移和视区追踪的图像显著区域检测.软件学报,2004,15(6)：891-898.
[48]张鹏,王润生.由底向上视觉注意中的层次性数据竞争.计算机辅助设计与图形学报,2005,17(8)：1667-1672.
[49]Aziz M Z, Mertsching B, Salah M, et al. Evaluation of Visual Attention Models for Robots. Proceedings of the Fourth IEEE International Conference on Computer Vision System,2006,20-20.
[50]Matthew D B, Jun S. A Neural Network Implementation of a Saliency Map Model. Neural Networks,2006,19:1467-1474.
[51]Carota L, Indiveri G, Dante V. A software-hardware selective attention system. Neurocomputing,2004,58-60:647-653.
[52]Park S J, Shin J K, Lee M. Biologically Inspired Saliency Map Model for Bottom-up Visual Attention. LNCS 2525, Springer,2002:418-426.
[53]Park S J, Ban S W, Sang S W. Implemention of Visual Attention System Using Bottom-up Saliency Map Model. ICANN/ICONIP 03, LNCS 2714, Springer,2003: 678-685.
[54]Walther D, Koch C. Modeling Attention to Salient Proto-Objects. Neural Networks. 2006,19(9):1394-1407.
[55]Walther D. Interactions of Visual Attention and Object Recognition:Computational Modeling. PhD Thesis,2006.
[56]Minka T P, Picard R. Interactive learning using a society of models. Pattern Recognition,1997,30(4):447-452.
[57]Ishikawa Y, Subramanya R, Faloutsos C. MindReader:Querying databases through multiple examples. Proc.VLDB, Aug,1998:98-109.
[58]Cox I J, Miller M L, MinLka T P, et al. The Bayesian image retrieval system, PicHunter:theory, implementation and Psychophysical experiments. IEEE Trans. On Image Processing,2000,9(1):20-37.
[59]Nastar C, Mitschke M, Meihac C. Efficient Query Refinement for Image Retrieval. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa Barbara, California, IEEE Computer Society,1998:547-552.
[60]Vailaya A, Jain A K, Zhang H J. On image classification:city images vs landscapes. Pattern Recognition,1998,31 (12):1921-1936.
[61]Vasconcelos N, Lippman A. A Bayesian framework for semantic content characterization. IEEE Conference on CVPR, June,1998:566-571.
[62]Vailaya A. Semantic classification in image databases. Ph.D thesis,2000.
[63]Su Z, Zhang H J. Relevance feedback using Bayesian classifier in content-based image retrieval. SPIE,2001:97-106
[64]Hong P, Tian Q, Huang T. Incorporate support vector machines to content-based image retrieval with relevance feedback. Proc.IEEE ICIP,2000:750-753.
[65]Tong S, Chang E. Support vector machine active learning for image retrieval. Proc. of ACM Multimedia, Ottawa, Canada,2001:107-118.
[66]Choi C H, Lyu M R. Group-based relevance feedback with support vector machine ensembles. In Proc.17th ICPR,2004:4-10.
[67]Jing F, Li M J, Zhang H J, et al. Entropy-based active learning with support vector machines for content-based image retrieval. IEEE ICME,2004,1:85-88.
[68]Zhang C, Chen T. An active learning framework for content-based information retrieval. IEEE Trans. On Multimedia,2002,4(2):260-268.
[69]Zhang C, Chen T. A new active learning approach for content-based information retrieval. AMP,2002,01-04.
[70]Tier K, Viola P. Boosting image retrieval. Proc. of CVPR,2000:530-534.
[71]Wu Y, Tian Q, Huang T S. Discriminant-EM algorithm with application to image retrieval. Proc. IEEE conf Computer Vision and Pattern Recognition, South Carolina, June, 2000,1:222-227.
[72]Zhou X S, Huang T S. Small sample learning during multimedia retrieval using BiasMap. IEEE conf. on CVPR,2001,1:11-17.
[73]Qian F, Li M J, Zhang L, et al. Gaussian mixture model for relevance feedback in image retrieval. IEEE conf. on ICME,2002,1:229-232.
[74]Wang J Z, Li J. Learning-based linguistic indexing of pictures with 2-D MHMMs. ACM conference on Multimedia,2002,436-445.
[75]Zhou D, Weston J, Gretton A, et al. Ranking on data manifolds. In Proc. of Advances in Neural Information Processing Systems (NIPS'03),2003:169-176.
[76]Li F, Dai Q, Xu W, et al. Correlated probabilistic label propagation for region-based image retrieval. In Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'07), Honolulu, Hawaii, USA, Apr.2007:789-792.
[77]Ahonen T, Matas J, He C, et al. Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. Image Analysis,16th Scandinavian Conference, SCIA 2009. Oslo, Norway, Lecture Notes in Computer Science,2009,5575:61-70.
[78]He X F, Niyogi P. Locality Preserving Projections[2003-12-08/2010-11-01]. http://books.nips.cc/nips16.html.
[79]沈晔,李敏丹,夏顺仁.基于内容的医学图像检索技术.计算机辅助设计与图 形学学报,2010,22(4)：569-578.
[80]陈善学,李方伟.矢量量化与图像处理.科学出版社,2009,144-146.
[81]Stricker M, Orengo M. Similarity of color images. SPIE Storage and Retrieval for Image and Video Databases Ⅲ,1995, vol.2185:381-392.
[82]Mao J, Jain A K. Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition,1992,25(2):173-188.
[83]John R, Smith, Shih-Fu Chang. Automated binary texture feature sets for image retrieval. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Proc., May 1996, 4:2239-2242.
[84]Robert M, Haralick, K. Shanmugam, et al. Texture features for image classification. IEEE Trans. On Sys, Man, and Cyb,1973, SMC-3(6):610-621.
[85]Tamura H, Mori S, Yamawaki T. Texture features corresponding to visual perception. IEEE Trans. On Systems, Man, and Cybernetics,1978,8(6):460-473.
[86]Niblack, et al. The QBIC project:querying images by content using color, texture, and shape. Proc. of SPIE, Storage and Retrieval for Image and Video Databases, San Jose, February 1993,1908:173-187.
[87]Chang T, Jay Kuo C C. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. On Image Processing,1993,2(4):429-441.
[88]Laine A, Fan J. Texture classification by wavelet packet signatures. IEEE Trans. Pattern Analysis and Machine Intelligence,1993,15(11):1186-1191.
[89]Zhang D S. Image retrieval based on shape. PhD Thesis,2002.
[90]石军,常义林.图像检索技术综述.西安电子科技大学学报(自然科学版),2003,30(4)：486-491.
[91]Jain A K. Fundamentals of Digital Image Processing. Englewood Cliffs, Prentice Hall,1989.
[92]Hu M K. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on,1962,8(2):179-187.
[93]杨翔英,章毓晋.小波轮廓描述符及在图像查询中的应用.计算机学报,1999,22(7)：752-757.
[94]姚玉荣,章毓晋.利用小波和矩进行基于形状的图像检索.中国图象图形学报,2000,SA(3)：206-210.
[95]Zhang H. J, Zhong D. A Scheme for visual feature-based image indexing. Proc. of SPIE conf. on Storage and Retrieval for Image and Video Databases Ⅲ, San Jose, Feb, 1995,36-46.
[96]Rui Y, Huang T S, Mehrotra S M, et al. Relevalce feedback:A power tool for interactive content-based image retrieval. IEEE Trnas Circuits and Systems for Video Technology.1998,8(5):644-655.
[97]Huang J, Kumar S R, Miart M, et.al. Image indexing using color correlograms. Porc. Computer vision and Pattern recognition.1997,762-768.
[98]Rui Y, Hunag T S. A novel relevance feedback technique in image retrieval. In:Proc ACM. Multimedia'99, oriando,1999,67-70.
[99]苏中,张宏江,马少平.基于贝叶斯分类器的图像检索相关反馈算法.软件学报,2002,13(10)：2001-2006.
[100]Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science,2000,290(12):2319-2323.
[101]Chu-Hong Hoi, Chi-Hang Chan, Kaizhu Huang, et.al. Biased support vector machine for relevance feedback in image retrieval. Porc. Int, Joint Conf Neural Networks, 2004,3189-3194.
[102]Guo-Dong Guo, Anil K.Jain, Wei-Ying, et.al. Learning Similarity Measure for Natural Image Retrieval With Relevance Feedback. IEEE Transactions on Neural Networks.2002.13(4):811-821.
[103]Yin P Y, Bhnua B, Chang K C, et.al. Integrating relevance feedback techniques for image retrieval using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(10):1536-1550.
[104]林立宇,张友焱,孙涛,秦前清Contourlet变换——影像处理应用.科学出版社,2008.
[105]Xie Hua, Pierce L E, Ulaby F T. SAR speckle reduction using wavelet denoising and Markov random field modeling. IEEE Transactionson Geoscience and Remote Sensing, 2002,40 (10):2196-2212.
[106]Feldman J A, Ballard D H. Connectionist models and their properties. Cognitive Science,1988:13-62.
[107]Do M N, Vetterli M. Contourlets:a directional multiresolution image representation. IEEE International Conference on Image Processing Rochester, September 2002,1:357-360.
[108]Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[109]Hotelling H. Analysis of A Complex of Statistical Variables into Principal components. Journal of Educational Psychology,1933,24(7):498-520.
[110]Manjunath B S, MaW Y. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18 (8):837-842.
[111]Manjunath B S, Ohm J R, Vasudevan V V, et al. Color and texture descriptors. IEEE Trans CSVT,2001,11(6):703-715.
[112]Pinheiro A M G. Image Description Using Scale-Space Edge Pixel Directions Histogram. Semantic Media Adaptation and Personalization, Second International Workshop, London, United kingdom, Inst of Elec and Elec Eng,2007,211-218.
[113]Antonini M, Barlaud M, Mathieu P, et al. Image coding using wavelet transform. IEEE Transactions on Image Processing,1992,1(2):205-220.
[114]Treisman A M, Gormican S. Feature analysis in early vision:evidence from search asymmetries. Psychology Review,1988,95(1):15-48.
[115]Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology,1962,160(1):106-154.
[116]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature,1996,381(13):607-609.
[117]Stentiford F. An attention based similarity measure with application to content-based information retrieval. Storage and Retrieval for Media Databases, Santa Clara, CA, USA, SPIE Electronic Imaging,2003,5021:221-232.
[118]刘丽,匡纲要.图像纹理特征提取方法综述.中国图象图形学报,2009,14(4)：622-635.
[119]Hayes K C, Shah A N, Rosenfeld A. Texture coarseness:further experiments. IEEE Transactions on Systems,1974, SMC-4(5):467-472.
[120]艾海舟,武勃等译.图像处理、分析与机器视觉.人民邮电出版社,2002：307-308.
[121]Mareschal I, Henrie J A, Shapley R M. A psychophysical correlate of contrast dependent changes in receptive field properties. Vision Research,2002,42(15):1879-1887.
[122]寿天德.视觉信息处理的脑机制.中国科学技术大学出版社,2010：22-49.
[123]Bamidele A, Stentiford F, Morphett J. An attention-based approach to content-based image retrieval. BT Technology Journal,2004,22(7):151-160.
[124]Siagian C, Itti L. Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell.2007,29(2):300-312.
[125]Smeulders Arnold W M, Worring M, Santini S, et al. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1349-1380.
[126]Zhou D, Bousquet O, Lal T N, et al. Learning with local and global consistency. In: Proc. of Advances in Neural Information Processing Systems 16, MIT Press,2004, 321-328.
[127]Zhou D, Weston J, Gretton A, et al. Ranking on Data Manifolds. In:Proc. of Advances in Neural Information Processing Systems 16, MIT Press,2004,169-176.
[128]Tong H H, HeJ, Li M, et al. Manifold-ranking based keyword propagation for image retrieval. EUASIP Journal of Applied Signal Processing, Special Issue on Information Mining from Multimedia Database,2006,21:1-10.
[129]He J, Li M, Zhang H J, et al. Manifold-ranking based image retrieval. In:Proc. of the 12th ACM Multimedia, New York, USA, Oct.2004:9-16.
[130]He J, Li M, Zhang H J, et al. Generalized manifold-ranking based image retrieval. IEEE Trans. on Image Processing,2006,15(10):3170-3177.
[131]Wang F, Zhang C S. Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering,2008,20(1):55-67.
[132]Zhu X, Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Pittsburgh, PA, USA:Carnegie Mellon University,2002.
[133]Chapelle O, Scholkopf B, Zien A. Semi-Supervised Learning. MIT Press,2006.
[134]Olga Kouropteva,Oleg Okun and Matti Pietikainen. Supervised Locally Linear Embedding Algorithm for Pattern Recognition. Lecture Notes in Computer Science,2003, Volume 2652/2003,386-394.
[135]Geng X, Zhan D C, Zhou Z H. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2005,35(6):1098-1107.
[136]Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Netw,1999,12:783-789,.
[137]Tong S, Chang E. Support vector machine active learning for image retrieval. Proceedings of the ACM International Conference on Multimedia. New York, NJ, USA: ACM,2002:107-118.
[138]王珏,周志华,周傲英.机器学习及其应用.清华大学出版社,2006.
[139]Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Proceedings of Advances in Neural Information Processing Systems 14. Cambridge, MA:MIT Press,2002:585-591.
[140]Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation,2003,15(6):1373-1396.
[141]Xiaofei He, Deng Cai. Learning a Maximum Margin Subspace for Image Retrieval. IEEE Trans. Knowledge and data engineering. February,2008,20(2):189-201.
[142]Masashi Sugiyama. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. Journal of Machachine Learning Research,2007, 8:1027-1061.
[143]Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science,2000,290:2323-2326.
[144]Skrobot V L, Castro E V R. Use of principal component analysis(PCA) and linear discriminant analysis(LDA) in gas chromatographic(GC) data in the investigation of gasoline adulteration. Energy & Fuels,2007,21(6):3394-3400.
[145]Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. PAMI, July 1997,19(7):711-720.
[146]Stefanos Z, Anastasios T, Ioannis P. Minimum class variance support vector machines.IEEE Trans on Image Processing,2007,16(10):2551-2563.
[147]杨健.线性投影分析的理论与算法及其在特征抽取中的应用.博士学位论文,南京理工大学,2002：58-69.
[148]Chung F. Spectral Graph Theory. American Mathematical Society,1997.
[149]Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning:Data Mining, Inference, and Prediction. New York:Springer-Verlag,2001,27(2):83-85.
[150]Cai D, He X, Han J. Semi-supervised discriminant analysis. in:Proceedings of the International Conference on Computer Vision,2007,1-7.
[151]Fei Wang, Changshui Zhang. Label Propagation through Linear Neighborhoods. Knowledge and Data Engineering,2008,20(1):55-67.
[152]Feiping Niea, ShimingXiang, YangqingJia, et al. Semi-supervised orthogonal discriminant analysis via label propagation. Pattern Recognition,2009,42:2615-2627.
[153]Jie Yu, Qi Tian. Semantic subspace projection and its applications in image retrieval. IEEE Transactions on Circuits and Systems for Video Technology,2008, 18(4):544-548.
[154]Zhou X S, Huang T S. Relevance feedback in image retrieval:a comprehensive review. ACM Multimedia Systems Journal,2003,8(6):536-544.
[2]Li J, Wang J Z, Wiedherold G. IRM:integrated region matching for image retrieval. Proceeding of ACM Multimedia,2000,147-156.
[3]Wang J Z, Li J, Wiederhold G. SIMPLIcity:semantics-sensitive integrated matching for Picture libraries.IEEE Trans. on PAMI,2001,23(9):1-17.
[4]Cllad C, Serge B, Hayit G, et al. Blockworld:image segmentation using expectation-maximization and its application to image querying. IEEE Trans on PAMI, 2002,24(8):1026-1038.
[5]袁昕,朱淼良.基于主色匹配的图像检索系统.计算机辅助设计与图形学学报,2000,12(12)：917-921.
[6]汪祖媛,梁栋,李斌.基于树状小波分解的纹理图像检索.中国图象图形学报,2001,6A(11)：1065-1069.
[7]何清法,李国杰.综合分块主色和相关反馈技术的图像检索方法.计算机辅助设计与图形学学报,2001,13(10)：912-917.
[8]王涛,胡事民,孙家广.基于颜色-空间的图像检索.软件学报,2002,13(10)：2031-2036.
[9]Lee H Y, Lee H K, Ha Y H. Spatial color descriptor for image retrieval and video segmentation. IEEE Trans. On Multimedia,2003,5(3):358-367.
[10]黄元元,郭丽,杨静宇.基于目标区域颜色与纹理特征的图像检索.南京理工大学学报,2003,27(3)：286-289.
[11]Qiu G P, Lam K M. Frequency layered color indexing for content-based image retrieval. IEEE Transactions on Image Processing,2003,12(1):102-113.
[12]洪安详.基于内容的图像检索若干论题研究.博士论文,浙江大学,2003.
[13]刘进,张天序.图像不变矩的推广.计算机学报,2004,27(5)：668-674.
[14]Ashley J, Barber R, Flickner M, et al. Automatic and semi-automatic methods for image annotation and retrieval in QBIC. SPIE 2420,1995, pp.24-35.
[15]Huang T S, Mehrotra S, Ranlchandran K. Multimedia analysis and retrieval system (MARS) project. In Proc. of 33dr Annual Clinic on Library Application of Data Processing Digital Image Access and Retrieval,1996.
[16]庄越挺,潘云鹤.基于内容的图像检索综述.模式识别与人工智能,1999,12(2)：170-177.
[17]Weber R, Schek H J, Blott S. A quantitative analysis and performance study forsimilarity-serch methods in high-dimensional spaces. Proc.24 Int. Conf. VLDB. New York:IEEE,1998,194-205.
[18]Eakins J P, Graham M E. Content-based image retrieval:a report to the JISC technology applications programme. Institute for image data researeh Newcastle-upon-tyne,1999,1-63.
[19]Rui Y, Huang T, Chang F. Image retrieval:Current techniques, promising directions, and open issues. Visual Comm. Image Represent,1999,10(1):39-62.
[20]李国辉,胡晓峰.基于内容的检索.计算机世界报,1998(18)78-85.
[21]Flickner M, Sawhney H, Niblack W, et al. Query by image and video content:the QBIC system. IEEE Computer,1995,28(9):23-32.
[22]Smith J R, Chang Shih-fu. VisualSEEK:a fully automated content-based image query system. [1996-07-23/2010-12-10]. http://citeseer.ist.psu.edu/smith96visualseek.htm 1.
[23]Bach J R, Fuller C, Gupta A, et al. The virage image search engine:an open framework for image management. Proc. SPIE Storage and Retrieval for Image and Video Database,1996,2670:76-87.
[24]Pentland A, Picard R W, Sclaroff S. Photobook:Content-Based manipulation of image databases. International Journal of Computer Vision,1996,18(3):233-254.
[25]Ogle V E, Stonebraker M. Chabot:retrieval from a relational database of images. IEEE Computer,1995,28(9):40-48.
[26]Chang S F, Chen W, Sundaram H. Semantic visual templates:Linking visual features to semantics. Proc. IEEE ICIP. Chicago:IL,1998,3:531-535.
[27]Lu Y, Hu C, Zhou X, et al. A unified framework for semantics and feature based relevance feedback in image retrieval systems. Proc of ACM Multimedia conference,2000, 31-37.
[28]Zhu X Q, Zhang H J. New query refinement and semantics integrated image retrieval system with semiautomatic annotation scheme. Journal of Electronic Imaging. 2001,10(4):850-860.
[29]Han J W, Guo L. A new image retrieval model supporting query by semantics and example. IEEE Conference on Image Processing. New York:IEEE,2002,3:953-956.
[30]Ynag J, Li Q, Zhuang Y T. Image retrieval and relevance feedback using peer indexing. Proc. IEEE conference on ICME,2002,409-412.
[31]Lim J H, Jin J S. Image indexing and retrieval using visual keyword histograms. Proe. IEEE conference on ICME,2002,1:213-216.
[32]Zhao R, Grosky W I. Negotiating the semantic gap:from feature maps to semantic landscapes. Pattern Recognition,2002,35(3):593-600.
[33]Zhou X S, Huang T S. Unifying keywords and visual contents in image retrieval. IEEE Multimedia,2002,9(2):23-33.
[34]Zhao R, Grosky W I. Narrowing the semantic gap-improved text-based web document retrieval using visual features. IEEE Trans. on Multimedia,2002,4(2):189-200.
[35]Tsotsos J K. Analyzing vision at the complexity level. Behavioral and Brain Sciences,1990,13:423-445.
[36]张菁,沈兰荪,David D F,基于视觉感知的图像检索的研究.电子学报,2008,36(3)：494-499.
[37]沈兰荪,张菁,李晓光.图像检索与压缩域处理技术的研究.北京：人民邮电出版社,2008：113-114.
[38]Treisman A M, Gelade G. A feature-integration theory of attention. Cognitive Psychology,1980,12(1):97-136.
[39]Koch C, Ullman S. Shifts in Selective Visual Attention:Towards the Underlying Neural Circuitry. Human Neurobiology,1985,4:219-227.
[40]Itti L, Koch C, Niebur E. A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998, 20(11):1254-1259.
[41]Itti L, Koch C. A Comparison of Feature Combination Strategies for Saliency-based Visual Attention Systems. In:Proc.SPIE Human Vision and Electronic Imaging IV(HVEI'99), San Jose, CA, Bellingham, WA:SPIE Press, Jan 1999, Vol.3644,pp.472-82.
[42]L.Itti. Models of Bottom-up and Top-down Visual Attention. PhD Thesis,2000.
[43]Itti L, Koch C. Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging,2001,10(1):161-169
[44]Itti L, Koch C. Computational Modeling of Visual Attention. Nature Reviews Neuroscience,2001,2(3):193-203.
[45]Sun Y, Fisher R. Object-based Visual Attention for Computer Vision. Artificial Intelligence,2003,146(1):77-123.
[46]Satoh S, Miyake S. A Model of Overt Visual Attention Based on Scale-Space Theory. Systems and Computers in Japan,2004,35(10):1-13.
[47]张鹏,王润生.基于视点转移和视区追踪的图像显著区域检测.软件学报,2004,15(6)：891-898.
[48]张鹏,王润生.由底向上视觉注意中的层次性数据竞争.计算机辅助设计与图形学报,2005,17(8)：1667-1672.
[49]Aziz M Z, Mertsching B, Salah M, et al. Evaluation of Visual Attention Models for Robots. Proceedings of the Fourth IEEE International Conference on Computer Vision System,2006,20-20.
[50]Matthew D B, Jun S. A Neural Network Implementation of a Saliency Map Model. Neural Networks,2006,19:1467-1474.
[51]Carota L, Indiveri G, Dante V. A software-hardware selective attention system. Neurocomputing,2004,58-60:647-653.
[52]Park S J, Shin J K, Lee M. Biologically Inspired Saliency Map Model for Bottom-up Visual Attention. LNCS 2525, Springer,2002:418-426.
[53]Park S J, Ban S W, Sang S W. Implemention of Visual Attention System Using Bottom-up Saliency Map Model. ICANN/ICONIP 03, LNCS 2714, Springer,2003: 678-685.
[54]Walther D, Koch C. Modeling Attention to Salient Proto-Objects. Neural Networks. 2006,19(9):1394-1407.
[55]Walther D. Interactions of Visual Attention and Object Recognition:Computational Modeling. PhD Thesis,2006.
[56]Minka T P, Picard R. Interactive learning using a society of models. Pattern Recognition,1997,30(4):447-452.
[57]Ishikawa Y, Subramanya R, Faloutsos C. MindReader:Querying databases through multiple examples. Proc.VLDB, Aug,1998:98-109.
[58]Cox I J, Miller M L, MinLka T P, et al. The Bayesian image retrieval system, PicHunter:theory, implementation and Psychophysical experiments. IEEE Trans. On Image Processing,2000,9(1):20-37.
[59]Nastar C, Mitschke M, Meihac C. Efficient Query Refinement for Image Retrieval. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa Barbara, California, IEEE Computer Society,1998:547-552.
[60]Vailaya A, Jain A K, Zhang H J. On image classification:city images vs landscapes. Pattern Recognition,1998,31 (12):1921-1936.
[61]Vasconcelos N, Lippman A. A Bayesian framework for semantic content characterization. IEEE Conference on CVPR, June,1998:566-571.
[62]Vailaya A. Semantic classification in image databases. Ph.D thesis,2000.
[63]Su Z, Zhang H J. Relevance feedback using Bayesian classifier in content-based image retrieval. SPIE,2001:97-106
[64]Hong P, Tian Q, Huang T. Incorporate support vector machines to content-based image retrieval with relevance feedback. Proc.IEEE ICIP,2000:750-753.
[65]Tong S, Chang E. Support vector machine active learning for image retrieval. Proc. of ACM Multimedia, Ottawa, Canada,2001:107-118.
[66]Choi C H, Lyu M R. Group-based relevance feedback with support vector machine ensembles. In Proc.17th ICPR,2004:4-10.
[67]Jing F, Li M J, Zhang H J, et al. Entropy-based active learning with support vector machines for content-based image retrieval. IEEE ICME,2004,1:85-88.
[68]Zhang C, Chen T. An active learning framework for content-based information retrieval. IEEE Trans. On Multimedia,2002,4(2):260-268.
[69]Zhang C, Chen T. A new active learning approach for content-based information retrieval. AMP,2002,01-04.
[70]Tier K, Viola P. Boosting image retrieval. Proc. of CVPR,2000:530-534.
[71]Wu Y, Tian Q, Huang T S. Discriminant-EM algorithm with application to image retrieval. Proc. IEEE conf Computer Vision and Pattern Recognition, South Carolina, June, 2000,1:222-227.
[72]Zhou X S, Huang T S. Small sample learning during multimedia retrieval using BiasMap. IEEE conf. on CVPR,2001,1:11-17.
[73]Qian F, Li M J, Zhang L, et al. Gaussian mixture model for relevance feedback in image retrieval. IEEE conf. on ICME,2002,1:229-232.
[74]Wang J Z, Li J. Learning-based linguistic indexing of pictures with 2-D MHMMs. ACM conference on Multimedia,2002,436-445.
[75]Zhou D, Weston J, Gretton A, et al. Ranking on data manifolds. In Proc. of Advances in Neural Information Processing Systems (NIPS'03),2003:169-176.
[76]Li F, Dai Q, Xu W, et al. Correlated probabilistic label propagation for region-based image retrieval. In Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'07), Honolulu, Hawaii, USA, Apr.2007:789-792.
[77]Ahonen T, Matas J, He C, et al. Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. Image Analysis,16th Scandinavian Conference, SCIA 2009. Oslo, Norway, Lecture Notes in Computer Science,2009,5575:61-70.
[78]He X F, Niyogi P. Locality Preserving Projections[2003-12-08/2010-11-01]. http://books.nips.cc/nips16.html.
[79]沈晔,李敏丹,夏顺仁.基于内容的医学图像检索技术.计算机辅助设计与图 形学学报,2010,22(4)：569-578.
[80]陈善学,李方伟.矢量量化与图像处理.科学出版社,2009,144-146.
[81]Stricker M, Orengo M. Similarity of color images. SPIE Storage and Retrieval for Image and Video Databases Ⅲ,1995, vol.2185:381-392.
[82]Mao J, Jain A K. Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition,1992,25(2):173-188.
[83]John R, Smith, Shih-Fu Chang. Automated binary texture feature sets for image retrieval. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Proc., May 1996, 4:2239-2242.
[84]Robert M, Haralick, K. Shanmugam, et al. Texture features for image classification. IEEE Trans. On Sys, Man, and Cyb,1973, SMC-3(6):610-621.
[85]Tamura H, Mori S, Yamawaki T. Texture features corresponding to visual perception. IEEE Trans. On Systems, Man, and Cybernetics,1978,8(6):460-473.
[86]Niblack, et al. The QBIC project:querying images by content using color, texture, and shape. Proc. of SPIE, Storage and Retrieval for Image and Video Databases, San Jose, February 1993,1908:173-187.
[87]Chang T, Jay Kuo C C. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. On Image Processing,1993,2(4):429-441.
[88]Laine A, Fan J. Texture classification by wavelet packet signatures. IEEE Trans. Pattern Analysis and Machine Intelligence,1993,15(11):1186-1191.
[89]Zhang D S. Image retrieval based on shape. PhD Thesis,2002.
[90]石军,常义林.图像检索技术综述.西安电子科技大学学报(自然科学版),2003,30(4)：486-491.
[91]Jain A K. Fundamentals of Digital Image Processing. Englewood Cliffs, Prentice Hall,1989.
[92]Hu M K. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on,1962,8(2):179-187.
[93]杨翔英,章毓晋.小波轮廓描述符及在图像查询中的应用.计算机学报,1999,22(7)：752-757.
[94]姚玉荣,章毓晋.利用小波和矩进行基于形状的图像检索.中国图象图形学报,2000,SA(3)：206-210.
[95]Zhang H. J, Zhong D. A Scheme for visual feature-based image indexing. Proc. of SPIE conf. on Storage and Retrieval for Image and Video Databases Ⅲ, San Jose, Feb, 1995,36-46.
[96]Rui Y, Huang T S, Mehrotra S M, et al. Relevalce feedback:A power tool for interactive content-based image retrieval. IEEE Trnas Circuits and Systems for Video Technology.1998,8(5):644-655.
[97]Huang J, Kumar S R, Miart M, et.al. Image indexing using color correlograms. Porc. Computer vision and Pattern recognition.1997,762-768.
[98]Rui Y, Hunag T S. A novel relevance feedback technique in image retrieval. In:Proc ACM. Multimedia'99, oriando,1999,67-70.
[99]苏中,张宏江,马少平.基于贝叶斯分类器的图像检索相关反馈算法.软件学报,2002,13(10)：2001-2006.
[100]Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science,2000,290(12):2319-2323.
[101]Chu-Hong Hoi, Chi-Hang Chan, Kaizhu Huang, et.al. Biased support vector machine for relevance feedback in image retrieval. Porc. Int, Joint Conf Neural Networks, 2004,3189-3194.
[102]Guo-Dong Guo, Anil K.Jain, Wei-Ying, et.al. Learning Similarity Measure for Natural Image Retrieval With Relevance Feedback. IEEE Transactions on Neural Networks.2002.13(4):811-821.
[103]Yin P Y, Bhnua B, Chang K C, et.al. Integrating relevance feedback techniques for image retrieval using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(10):1536-1550.
[104]林立宇,张友焱,孙涛,秦前清Contourlet变换——影像处理应用.科学出版社,2008.
[105]Xie Hua, Pierce L E, Ulaby F T. SAR speckle reduction using wavelet denoising and Markov random field modeling. IEEE Transactionson Geoscience and Remote Sensing, 2002,40 (10):2196-2212.
[106]Feldman J A, Ballard D H. Connectionist models and their properties. Cognitive Science,1988:13-62.
[107]Do M N, Vetterli M. Contourlets:a directional multiresolution image representation. IEEE International Conference on Image Processing Rochester, September 2002,1:357-360.
[108]Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[109]Hotelling H. Analysis of A Complex of Statistical Variables into Principal components. Journal of Educational Psychology,1933,24(7):498-520.
[110]Manjunath B S, MaW Y. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18 (8):837-842.
[111]Manjunath B S, Ohm J R, Vasudevan V V, et al. Color and texture descriptors. IEEE Trans CSVT,2001,11(6):703-715.
[112]Pinheiro A M G. Image Description Using Scale-Space Edge Pixel Directions Histogram. Semantic Media Adaptation and Personalization, Second International Workshop, London, United kingdom, Inst of Elec and Elec Eng,2007,211-218.
[113]Antonini M, Barlaud M, Mathieu P, et al. Image coding using wavelet transform. IEEE Transactions on Image Processing,1992,1(2):205-220.
[114]Treisman A M, Gormican S. Feature analysis in early vision:evidence from search asymmetries. Psychology Review,1988,95(1):15-48.
[115]Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology,1962,160(1):106-154.
[116]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature,1996,381(13):607-609.
[117]Stentiford F. An attention based similarity measure with application to content-based information retrieval. Storage and Retrieval for Media Databases, Santa Clara, CA, USA, SPIE Electronic Imaging,2003,5021:221-232.
[118]刘丽,匡纲要.图像纹理特征提取方法综述.中国图象图形学报,2009,14(4)：622-635.
[119]Hayes K C, Shah A N, Rosenfeld A. Texture coarseness:further experiments. IEEE Transactions on Systems,1974, SMC-4(5):467-472.
[120]艾海舟,武勃等译.图像处理、分析与机器视觉.人民邮电出版社,2002：307-308.
[121]Mareschal I, Henrie J A, Shapley R M. A psychophysical correlate of contrast dependent changes in receptive field properties. Vision Research,2002,42(15):1879-1887.
[122]寿天德.视觉信息处理的脑机制.中国科学技术大学出版社,2010：22-49.
[123]Bamidele A, Stentiford F, Morphett J. An attention-based approach to content-based image retrieval. BT Technology Journal,2004,22(7):151-160.
[124]Siagian C, Itti L. Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell.2007,29(2):300-312.
[125]Smeulders Arnold W M, Worring M, Santini S, et al. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1349-1380.
[126]Zhou D, Bousquet O, Lal T N, et al. Learning with local and global consistency. In: Proc. of Advances in Neural Information Processing Systems 16, MIT Press,2004, 321-328.
[127]Zhou D, Weston J, Gretton A, et al. Ranking on Data Manifolds. In:Proc. of Advances in Neural Information Processing Systems 16, MIT Press,2004,169-176.
[128]Tong H H, HeJ, Li M, et al. Manifold-ranking based keyword propagation for image retrieval. EUASIP Journal of Applied Signal Processing, Special Issue on Information Mining from Multimedia Database,2006,21:1-10.
[129]He J, Li M, Zhang H J, et al. Manifold-ranking based image retrieval. In:Proc. of the 12th ACM Multimedia, New York, USA, Oct.2004:9-16.
[130]He J, Li M, Zhang H J, et al. Generalized manifold-ranking based image retrieval. IEEE Trans. on Image Processing,2006,15(10):3170-3177.
[131]Wang F, Zhang C S. Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering,2008,20(1):55-67.
[132]Zhu X, Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Pittsburgh, PA, USA:Carnegie Mellon University,2002.
[133]Chapelle O, Scholkopf B, Zien A. Semi-Supervised Learning. MIT Press,2006.
[134]Olga Kouropteva,Oleg Okun and Matti Pietikainen. Supervised Locally Linear Embedding Algorithm for Pattern Recognition. Lecture Notes in Computer Science,2003, Volume 2652/2003,386-394.
[135]Geng X, Zhan D C, Zhou Z H. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2005,35(6):1098-1107.
[136]Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Netw,1999,12:783-789,.
[137]Tong S, Chang E. Support vector machine active learning for image retrieval. Proceedings of the ACM International Conference on Multimedia. New York, NJ, USA: ACM,2002:107-118.
[138]王珏,周志华,周傲英.机器学习及其应用.清华大学出版社,2006.
[139]Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Proceedings of Advances in Neural Information Processing Systems 14. Cambridge, MA:MIT Press,2002:585-591.
[140]Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation,2003,15(6):1373-1396.
[141]Xiaofei He, Deng Cai. Learning a Maximum Margin Subspace for Image Retrieval. IEEE Trans. Knowledge and data engineering. February,2008,20(2):189-201.
[142]Masashi Sugiyama. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. Journal of Machachine Learning Research,2007, 8:1027-1061.
[143]Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science,2000,290:2323-2326.
[144]Skrobot V L, Castro E V R. Use of principal component analysis(PCA) and linear discriminant analysis(LDA) in gas chromatographic(GC) data in the investigation of gasoline adulteration. Energy & Fuels,2007,21(6):3394-3400.
[145]Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. PAMI, July 1997,19(7):711-720.
[146]Stefanos Z, Anastasios T, Ioannis P. Minimum class variance support vector machines.IEEE Trans on Image Processing,2007,16(10):2551-2563.
[147]杨健.线性投影分析的理论与算法及其在特征抽取中的应用.博士学位论文,南京理工大学,2002：58-69.
[148]Chung F. Spectral Graph Theory. American Mathematical Society,1997.
[149]Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning:Data Mining, Inference, and Prediction. New York:Springer-Verlag,2001,27(2):83-85.
[150]Cai D, He X, Han J. Semi-supervised discriminant analysis. in:Proceedings of the International Conference on Computer Vision,2007,1-7.
[151]Fei Wang, Changshui Zhang. Label Propagation through Linear Neighborhoods. Knowledge and Data Engineering,2008,20(1):55-67.
[152]Feiping Niea, ShimingXiang, YangqingJia, et al. Semi-supervised orthogonal discriminant analysis via label propagation. Pattern Recognition,2009,42:2615-2627.
[153]Jie Yu, Qi Tian. Semantic subspace projection and its applications in image retrieval. IEEE Transactions on Circuits and Systems for Video Technology,2008, 18(4):544-548.
[154]Zhou X S, Huang T S. Relevance feedback in image retrieval:a comprehensive review. ACM Multimedia Systems Journal,2003,8(6):536-544.