基于内容图像检索的关键技术研究
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摘要
随着多媒体技术的不断发展,日常生活中的图像数量呈现爆炸性的增长,图像检索已成为当前亟需解决的问题。目前常用的图像检索方式与文本信息检索一致,均是通过关键词来进行检索。这种方式一般被称为基于文本的图像检索,其中主要存在两方面问题：第一、该检索方式需要对图像进行合理的关键词标注,然而当前图像自动标注技术尚不成熟；第二、用户有时很难用若干关键词来表示检索条件,而更希望通过以图搜图的方式来查找相似图像。为了解决这些问题,进一步满足用户的检索需求,基于内容的图像检索技术被提出。该技术是直接通过对图像内容进行分析来提取相应的视觉特征,以此实现检索。当前基于内容的图像检索技术已成为学术界与工业界所关注的焦点。本文将针对该领域开展深入而细致的研究,重点关注基于形状特征的图像检索,基于颜色特征的图像检索,和图像检索中的用户反馈处理三项关键技术,主要工作包括：
(1)在基于形状特征的图像检索方面,本文提出一种结合形状描述与特征匹配的新检索方案用来解决商标图像检索问题。在新方案中,首先为了进一步准确表示形状相邻边界点间的关系,提出一种基于外接圆采样的新轮廓特征描述算子；其次,为了准确描述形状区域特征并降低计算复杂度,提出基于兴趣点及其空间分布的新区域特征描述算子；最后,为了克服当前特征匹配策略的缺陷,提出一种基于调整值的特征匹配策略,并给出基于条件概率的调整值计算方法。文中通过在公共形状图像库上的图像检索实验来对该方案及其所包含的特征描述算子和特征匹配策略进行全面性验证。通过实验结果对比可以看出,所提出的方案要明显优于当前其他方案,能更有效地应用于商标图像检索中。
(2)在基于颜色特征的图像检索方面,本文提出两种方案来解决在基于局部颜色直方图的检索方法中由游离兴趣点所产生的问题。第一种方案是通过选择更为合理的兴趣点中心来作为兴趣点空间环形划分的基准,从而更为合理地构建局部颜色直方图实现检索。按照这一思路,本文给出了基于兴趣区域的检索方法和基于兴趣点最小圆的检索方法。第二种方案是在聚类算法基础上对兴趣点进行分组来构建局部颜色直方图实现检索。按照这一思路,本文给出基于兴趣点加权聚类的检索方法,其中加权聚类算法是对传统聚类算法的有效改进,目的是得到更好的兴趣点分组结果。文中通过在公共标准图像库上的检索实验,验证了上述三种检索方法的有效性。并通过与当前同类方法进行对比可以看出,所提出的三种方法均能有效地解决游离兴趣点问题,更为合理地构建局部颜色直方图,从而获得更为精确的检索结果。
(3)在图像检索中的用户反馈处理方面,本文提出一种基于邻接矩阵和局部组合特征的新型核函数,来进一步提升基于支持向量机(Support Vector Machine, SVM)的用户反馈处理方法的有效性。首先在现有局部特征表示方法基础上融入邻接矩阵特征和局部组合特征从而能更细致地表示图像内容。其次,给出新核函数的定义,其计算表达式分为两部分,一部分是传统核函数的线性组合计算,另一部分是基于邻接最佳匹配对的计算。其中邻接最佳匹配对是新提出的概念,目的是为了使核函数的计算结果能更有效地表示图像间的相似性。最后,证明所提出的核函数满足应具有的性质,从而可应用于SVM中进行用户反馈处理。文中通过两个公共标准图像库上的目标对象检索实验来对所提出的核函数进行验证。通过实验结果比较可以看出,与当前其他常用核函数相比,本文所提出的核函数能有效地融入SVM中进行用户反馈处理,从而获取更为精确的图像检索结果。
With the explosive growth of images, image retrieval has drawn more and more attention. Like textual information retrieval, existing methods for image retrieval are built upon the "query by keywords", which are referred to text-based image retrieval. In text-based image retrieval, images should be annotated by keywords. However, automatic image annotation is still chal-lenging. Moreover, as it is difficult to exactly describe the query image by several keywords, users prefer to give a sample image as the query to find similar images. To achieve this goal, content-based image retrieval is proposed, in which image retrieval is based on the vision fea-tures extracted from image content. There is a growing interest on content-based image retrieval from both academia and industry. In this dissertation, we focus on three key techniques in content-based image retrieval:shape-based retrieval, color-based retrieval, and user feedback processing. The main contributions of this dissertation are summarized as follows:
(1) In the shape-based image retrieval, we propose an effective solution for trademark im-age retrieval by combining shape description and feature matching. First, we propose a new kind of contour-based shape descriptor, which can describe the relationship between two adja-cent boundary points more effectively than other existing descriptors. Second, we propose a new kind of region-based shape descriptor based on interest points and their spatial distribution to describe the region-based shape feature effectively. Finally, we propose a new kind of feature matching strategy to overcome the drawbacks of existing strategies, in which the parameter for correcting the dissimilarities between two images can be computed by a conditional probability based method. We conduct a large number of experiments to evaluate the performances of the proposed solution, the proposed shape description method and the proposed feature matching strategy, respectively. The experimental results show that the proposed solutions outperform existing solutions for trademark image retrieval.
(2) In the color-based image retrieval, we propose two solutions to address the problem of isolated interest points in the images when building local color histogram. The first solution is to choose the new center of interest points as the basis of interest points grouping for building local color histogram. According to this idea, we present two methods based on the interest region and the minimal circle of interest points, respectively. The second solution is to group the inter-est points by clustering for building local color histogram. According to this idea, we present a method based on the weighted clustering of interest points, in which the weighted clustering al-gorithm is proposed to group the interest points more effectively. We conduct a large number of experiments on the public image database to evaluate the performances of these proposed meth-ods. The experimental results show that the proposed methods can address the existing problem in image retrieval based on the local color histogram to get more precise retrieval results.
(3) In the user feedback processing, we propose a novel kernel function based on adja-cency matrix and local combined features to improve the support vector machine (SVM) based user feedback processing method. First, adjacency matrix and local combined features are in-corporated into the existing local-based representation method to describe the image content effectively. Second, the novel kernel function is proposed, which consists of two parts:one is the linear combination of traditional kernel functions, and the other is the computation of adja-cent best matched pairs. In this kernel function, the adjacent best matched pair is proposed to compute the similarities between images more effectively. Finally, we verify that the proposed kernel function can be used in SVM to process the user feedback. We conduct a large number of experiments on two public image databases to evaluate the performance of processing the user feedback by the proposed kernel function. The experimental results show that the proposed kernel function can be incorporated into SVM to process user feedback more effectively. After user feedback processing, we can get more precise retrieval results.
(1)在基于形状特征的图像检索方面,本文提出一种结合形状描述与特征匹配的新检索方案用来解决商标图像检索问题。在新方案中,首先为了进一步准确表示形状相邻边界点间的关系,提出一种基于外接圆采样的新轮廓特征描述算子；其次,为了准确描述形状区域特征并降低计算复杂度,提出基于兴趣点及其空间分布的新区域特征描述算子；最后,为了克服当前特征匹配策略的缺陷,提出一种基于调整值的特征匹配策略,并给出基于条件概率的调整值计算方法。文中通过在公共形状图像库上的图像检索实验来对该方案及其所包含的特征描述算子和特征匹配策略进行全面性验证。通过实验结果对比可以看出,所提出的方案要明显优于当前其他方案,能更有效地应用于商标图像检索中。
(2)在基于颜色特征的图像检索方面,本文提出两种方案来解决在基于局部颜色直方图的检索方法中由游离兴趣点所产生的问题。第一种方案是通过选择更为合理的兴趣点中心来作为兴趣点空间环形划分的基准,从而更为合理地构建局部颜色直方图实现检索。按照这一思路,本文给出了基于兴趣区域的检索方法和基于兴趣点最小圆的检索方法。第二种方案是在聚类算法基础上对兴趣点进行分组来构建局部颜色直方图实现检索。按照这一思路,本文给出基于兴趣点加权聚类的检索方法,其中加权聚类算法是对传统聚类算法的有效改进,目的是得到更好的兴趣点分组结果。文中通过在公共标准图像库上的检索实验,验证了上述三种检索方法的有效性。并通过与当前同类方法进行对比可以看出,所提出的三种方法均能有效地解决游离兴趣点问题,更为合理地构建局部颜色直方图,从而获得更为精确的检索结果。
(3)在图像检索中的用户反馈处理方面,本文提出一种基于邻接矩阵和局部组合特征的新型核函数,来进一步提升基于支持向量机(Support Vector Machine, SVM)的用户反馈处理方法的有效性。首先在现有局部特征表示方法基础上融入邻接矩阵特征和局部组合特征从而能更细致地表示图像内容。其次,给出新核函数的定义,其计算表达式分为两部分,一部分是传统核函数的线性组合计算,另一部分是基于邻接最佳匹配对的计算。其中邻接最佳匹配对是新提出的概念,目的是为了使核函数的计算结果能更有效地表示图像间的相似性。最后,证明所提出的核函数满足应具有的性质,从而可应用于SVM中进行用户反馈处理。文中通过两个公共标准图像库上的目标对象检索实验来对所提出的核函数进行验证。通过实验结果比较可以看出,与当前其他常用核函数相比,本文所提出的核函数能有效地融入SVM中进行用户反馈处理,从而获取更为精确的图像检索结果。
With the explosive growth of images, image retrieval has drawn more and more attention. Like textual information retrieval, existing methods for image retrieval are built upon the "query by keywords", which are referred to text-based image retrieval. In text-based image retrieval, images should be annotated by keywords. However, automatic image annotation is still chal-lenging. Moreover, as it is difficult to exactly describe the query image by several keywords, users prefer to give a sample image as the query to find similar images. To achieve this goal, content-based image retrieval is proposed, in which image retrieval is based on the vision fea-tures extracted from image content. There is a growing interest on content-based image retrieval from both academia and industry. In this dissertation, we focus on three key techniques in content-based image retrieval:shape-based retrieval, color-based retrieval, and user feedback processing. The main contributions of this dissertation are summarized as follows:
(1) In the shape-based image retrieval, we propose an effective solution for trademark im-age retrieval by combining shape description and feature matching. First, we propose a new kind of contour-based shape descriptor, which can describe the relationship between two adja-cent boundary points more effectively than other existing descriptors. Second, we propose a new kind of region-based shape descriptor based on interest points and their spatial distribution to describe the region-based shape feature effectively. Finally, we propose a new kind of feature matching strategy to overcome the drawbacks of existing strategies, in which the parameter for correcting the dissimilarities between two images can be computed by a conditional probability based method. We conduct a large number of experiments to evaluate the performances of the proposed solution, the proposed shape description method and the proposed feature matching strategy, respectively. The experimental results show that the proposed solutions outperform existing solutions for trademark image retrieval.
(2) In the color-based image retrieval, we propose two solutions to address the problem of isolated interest points in the images when building local color histogram. The first solution is to choose the new center of interest points as the basis of interest points grouping for building local color histogram. According to this idea, we present two methods based on the interest region and the minimal circle of interest points, respectively. The second solution is to group the inter-est points by clustering for building local color histogram. According to this idea, we present a method based on the weighted clustering of interest points, in which the weighted clustering al-gorithm is proposed to group the interest points more effectively. We conduct a large number of experiments on the public image database to evaluate the performances of these proposed meth-ods. The experimental results show that the proposed methods can address the existing problem in image retrieval based on the local color histogram to get more precise retrieval results.
(3) In the user feedback processing, we propose a novel kernel function based on adja-cency matrix and local combined features to improve the support vector machine (SVM) based user feedback processing method. First, adjacency matrix and local combined features are in-corporated into the existing local-based representation method to describe the image content effectively. Second, the novel kernel function is proposed, which consists of two parts:one is the linear combination of traditional kernel functions, and the other is the computation of adja-cent best matched pairs. In this kernel function, the adjacent best matched pair is proposed to compute the similarities between images more effectively. Finally, we verify that the proposed kernel function can be used in SVM to process the user feedback. We conduct a large number of experiments on two public image databases to evaluate the performance of processing the user feedback by the proposed kernel function. The experimental results show that the proposed kernel function can be incorporated into SVM to process user feedback more effectively. After user feedback processing, we can get more precise retrieval results.
引文
[1]Lew M, Sebe N, Djeraba C, et al. Content-based multimedia information retrieval:State of the art and challenges[J]. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP),2006,2(1):1-19.
[2]Arnold W, Worring M, Santini S, et al. Content:based image retrieval at the end of the early years[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2000,22(12):38-45.
[3]Swain M, Ballard D. Color indexing[J]. International Journal of Computer Vision (IJCV),1991, 7(1):11-32.
[4]Hafner J, Sawhney H. Efficient color histogram indexing for quadratic form distance functions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1995,17(7):729-736.
[5]Funt B, Finlayson G. Color constant color indexing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1995,17(5):522-529.
[6]Wan X, Kuo C C. A new approach to image retrieval with hierarchical color clustering[J]. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT),1998,8(5):628-643.
[7]曹莉华,柳伟,李国辉.基于多种主色调的图像检索算法研究与实现[J].计算机研究与发展,1999,36(1)：96-100.
[8]Han J, Ma K K. Fuzzy color histogram:An efficient color feature for image indexing and retrieval[C]. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE 2000:2011-2014.
[9]Han J, Ma K K. Fuzzy color histogram and its use in color image retrieval[J]. IEEE Transactions on Image Processing (TIP),2002,11(8):944-952.
[10]Strieker M, Dimai A. Spectral covariance and fuzzy regions for image indexing[J]. Machine Vision and Applications,1997,10(2):66-73.
[11]Huang J, Kumar S, Mitra M, et al. Image indexing using color correlograms[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 1997:762-768.
[12]Huang J, Ravikumar S, Mitra M, et al. Spatial color indexing and application[J]. International Journal of Computer Vision (IJCV),1999,35(3):245-268.
[13]Pass G, Zabih R, Miller J. Comparing images using color coherence vectors[C]. Proceedings of the 4th ACM International Conference on Multimedia (MM). ACM 1996:65-73.
[14]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[J].计算机辅助设计与图形学学报,2001,13(10)：912-917.
[15]Li X. Image retrieval based on perceptive weighted color blocks[J]. Pattern Recognition Letters,2003, 24(12):1935-1941.
[16]丁贵广,戴琼海,徐立文.基于兴趣点局部分布特征的图像检索方法[J].光电子.激光,2005,16(9)：1101-1106.
[17]苏小红,丁进,马培军.基于兴趣点凸包和svm加权反馈实现图像检索[J].计算机学报,2009,32(11)：2221-2228.
[18]Weijer J, Schmid C, Verbeek J, et al. Learning color names for real-world applications [J]. IEEE Transactions on Image Processing (TIP),2009,18(7):1512-1523.
[19]Aptoula E, Lefevre S. Morphological description of color images for content-based image retrieval[J]. IEEE Transactions on Image Processing (TIP),2009,18(11):2505-2517.
[20]Chen W T, Liu W C, Chen M S. Adaptive color feature extraction based on image color distributions[J]. IEEE Transactions on Image Processing (TIP),2010,19(8):2005-2016.
[21]Wengert C, Douze M, Jegou H. Bag-of-colors for improved image search[C]. Proceedings of the 19th ACM International Conference on Multimedia (MM). New York, USA:ACM,2011:1437-1440.
[22]Zhang D, Lu G. Review of shape representation and description techniques[J]. Pattern Recognition, 2004,37(1):1-19.
[23]Iivarinen J, Peura M, Srel J, et al. Comparison of combined shape descriptors for irregular objects[C]. Proceedings of the 8th British Machine Vision Conference (BMVC). Essex, Great Britain:,1997: 430-439.
[24]Vailaya A, Zhong Y, Jain A. A hierarchical system for efficient image retrieval[C]. Proceedings of International Conference on Pattern Recognition (ICPR).1996:356-360.
[25]Rangayyan R, El-Faramawy N, Desautels J, et al. Measures of acutance and shape for classification of breast tumors[J]. IEEE Transactions on Medical Imaging (TMI),1997,16(6):799-810.
[26]邬浩,潘云鹤,庄越挺,等.基于对象形状的图像查询技术[J].软件学报,1998,9(5)：344349.
[27]庄越挺.智能多媒体信息分析与检索的研究[D].博士学位论文.浙江大学,1998.
[28]Berretti S, Bimbo A, Pala P. Retrieval by shape similarity with perceptual distance and effective index-ing[J]. IEEE Transactions on Multimedia,2000,2(4):225-239.
[29]Teh C, Chin R. On image analysis by the methods of moments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1988,10(4):496-513.
[30]Liao S, Pawlak M. On image analysis by moments [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1996,18(3):254-266.
[31]Teague M. Image analysis via the general theory of moments [J].'Journal of the Optical Society of America (JOSA),1980,70(8):920-930.
[32]Zhang D, Lu G. Improving retrieval performance of zernike moment descriptor on affined shapes[C]. Proceedings of International Conference on Multimedia and Expo. Lausanne, Switzerland:,2002: 205-208.
[33]Papacostas G, Boutalis Y, Karras D, et al. A new class of zernike moments for computer vision appli-cations[J]. Information Science,2007,177(13):731-742.
[34]王冰,刘晓霞,耿国华,等.一种基于补偿法则的矩的快速算法[J1.计算机研究与发展,2003,40(7)：1042-1048.
[35]Wee C, Paramesran R. On the computational aspects of zernike moments[J]. Image and Vision Com-puting,2007,25(6):967-980.
[36]Peter K, Michael D, Horst B. Beyond pairwise shape similarity analysis [C]. Proceedings of Asian Conference on Computer Vision (ACCV). Springer 2009:655-666.
[37]Bai X, Yang X, Latecki L, et al. Learning context-sensitive shape similarity by graph transduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2010,32(5):861-874.
[38]Yang X, Latecki L. Affinity learning on a tensor product graph with applications to shape and image re-trieval[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2011:2369-2376.
[39]He D, Wang L. Texture unit, texture spectrum, and texture analysis[J]. IEEE Transactions on Geo-science and Remote Sensing,1990,28(4):509-512.
[40]Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classification based on feature distributions[J]. Pattern Recognition,1996,29(1):51-59.
[41]Ojala T, Pietikainen M, Harwood D. Multiresolution gray scale and rotation invariant texture classifi-cation with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2002,24(7):971-987.
[42]Brochard J, Khoudeir M, Augereau B. Invariant feature extraction for 3d texture analysis using the autocorrelation function[J]. Pattern Recognition,2001,22(6-7):759-768.
[43]Huang Y, Chan K. Texture decomposition by harmonics extraction from higher order statistics[J]. IEEE Transactions on Image Processing (TIP),2004,13(1):1-14.
[44]Robert A, Wang J, Laurent Y. Texture classification using windowed fourier filters[J]. IEEE Transac-tions on Pattern Analysis and Machine Intelligence (TPAMI),1997,19(2):148-153.
[45]Laine A, Fan J. Texture classification by wavelet packet signatures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1993,15(11):1186-1191.
[46]Dunn D, Higgins W, Wakeley J. Texture segmentation using 2-d gabor elementary functions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1994,16(2):130-148.
[47]Chan C, Pang G. Fabric defect detection by fourier analysis[J]. IEEE Transactions on Industry Appli-cations,2000,36(5):1267-1276.
[48]Tsai D, Heish C. Automated surface inspection for directional textures[J]. Image and Vision Comput-ing,1999,18(1):49-62.
[49]Kumar A, Pang G. Defect detection in textured materials using glabor filters[J]. IEEE Transactions on Industry Applications,2002,38(2):425-440.
[50]Bodnarova A, Bennamoun M, Latham S. Optimal glabor filters for textile flaw detection[J]. Pattern Recognition,2002,35(12):2973-2991.
[51]Tsai D, Wu S. Automated surface inspection using gabor filters[J]. International Journal of Advanced Manufacturing Technology,2000,16(7):474-482.
[52]Manjunath B, Ma W. Texture features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1996,18(8):837-842.
[53]Michael D, Faouzi K. Reversible integer-to-integer wavelet transforms for image compression:Per-formance evaluation and analysis[J]. IEEE Transactions on Image Processing (TIP),2000,9(6):1010-1024.
[54]Thyagarajan K, Tom N, Charles P. A maximum likelihood approach to texture classification using wavelet transform[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 1994:640-644.
[55]Chen J, Jain A. A structural approach to identify defects in texture images[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. IEEE 1988:29-32.
[56]Bodnarova A, Bennamoun M, Kubik K. Defect detection in textile materials based on aspects of the hvs[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. IEEE 1988: 4423-4428.
[57]Chetverikov D, Hanbury A. Finding defects in texture using regularity and local orientation [J]. Pattern Recognition,2002,35(10):2165-2180.
[58]Cross G, Jain A. Markov random field texture models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1983,5(1):25-39.
[59]Pentland A. Fractal-based description of natural scenes [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1984,6(6):661-674.
[60]Serafim A. Segmentation of natural images based on multiresolution pyramids linking of the parameters of an autoregressive rotation invariant model, application to leather defect detection[C]. Proceedings of International Conference on Pattern Recognition (ICPR).1992:41-44.
[61]Krishnamachari S, Chellappa R. Multiresolution gauss-markov random field models for texture seg- mentation[J]. IEEE Transactions on Image Processing (TIP),1997,6(2):251-267.
[62]Anh V, Maeda J, Tieng Q. Multifractal texture analysis and classification[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 1999:445-449.
[63]Wallraven C, Caputo B. Recognition with local features:the kernel recipe[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2003:257-264.
[64]Moravec H. Towards automatic visual obstacle avoidance[C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence (IJCAI). Cambridge, USA:,1977:584-584.
[65]Asada H, Brady M. The curvature primal sketch[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1986,8(1):2-14.
[66]Harris C, Stephens M. Combined corner and edge detector[C]. Proceedings of the 4th Alvey Vision Conference.1988:147-151.
[67]Horaud R, Skordas T, Veillon F. Finding geometric and relational structures in an image[C]. Proceed-ings of the 1st European Conference on Computer Vision (ECCV). Antibes, France:,1990:374-384.
[68]Rohr K. Recognizing corners by fitting parametric models[J]. International Journal of Computer Vision (IJCV),1992,9(3):213-230.
[69]Forstner W. A framework for low level feature extraction[C]. Proceedings of the 3rd European Confer-ence on Computer Vision (ECCV). Stockholm, Sweden:,1994:383-394.
[70]Lowe D. Object recognition from local scale-invariant features[J]. Proceedings of IEEE International Conference on Computer Vision (ICCV),1999,2:1150-1157.
[71]Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors [J]. International Journal of Computer Vision (IJCV),2004,60(l):63-86.
[72]Lowe D. Distinctive image features from scale-invariant keypoints[J]. International Journal of Com-puter Vision (IJCV),2004,60(2):91-110.
[73]Jiang Y, Yang J, Ngo C, et al. Representations of keypoint-based semantic concept detection:A com-prehensive study [J]. IEEE Transactions on Multimedia,2010,12(1):42-53.
[74]Sivic J, Zisserman A. Video google:A text retrieval approach to object matching in videos[C]. Pro-ceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2003:1470-1477.
[75]Nister D, Stewenius H. Scalable recognition with a vocabulary tree[C]. Proceedings of IEEE Interna-tional Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2006:2161-2168.
[76]Philbin J, Chum O, Isard M, et al. Object retrieval with large vocabularies and fast spatial matching [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2007:1-8.
[77]Tuytelaars T, Schmid C. Vector quantizing feature space with a regular lattice[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2007:1-8.
[78]Philbin J, Chum O, Isard M, et al. Lost in quantization:Improving particular object retrieval in large scale image databases[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2008:1-8.
[79]Wu Z, Ke Q, Sun J, et al. A multi-sample, multi-tree approach to bag-of-words image representation for image retrieval[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2009:1992-1999.
[80]Cao Y, Wang C, Li Z, et al. Spatial-bag-of-features[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2010:3352-3359.
[81]Zhang Y, Jia Z, Chen T. Image retrieval with geometry preserving visual phrases[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2011: 809-816.
[82]Liu J, Yang Y, Shah M. Learning semantic visual vocabularies using diffusion distance[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2009: 461-468.
[83]Zhang S, Huang Q, Hua G, et al. Building contextual visual vocabulary for large-scale image applica-tions[C]. In Proceedings of the 18th ACM International Conference on Multimedia (MM). ACM 2010: 501-510.
[84]Ji R, Yao H, Sun X, et al. Towards semantic embedding in visual vocabulary [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2010:918-925.
[85]Enser P, Sandom C. Towards a comprehensive survey of the semantic gap in visual image retrieval [C]. Proceedings of ACM International Conference on Image and Video Retrieval (CIVR). ACM 2003: 291-299.
[86]吴洪,卢汉清,马颂德.基于内容图像检索中相关反馈技术的回顾[J].计算机学报,2005,28(12)：1969-1979.
[87]Huang J, et al. Combining supervised learning with color correlograms for content-based image re-trieval[C]. In Proceedings of the ACM International Conference on Multimedia (MM). ACM 1997: 325-334.
[88]Zhong S, Zhang H, Li S, et al. Relevance feedback in content-based image retrieval:Bayesian frame-work, feature subspaces and progressive learning [J]. IEEE Transactions on Image Processing (TIP), 2003,12(8):924-937.
[89]Rui Y, Huang T, Mehrotra S, et al. A relevance feedback architecture for content-based multimedia information retrieval systems[C]. In Proceedings of IEEE Workshop on Content-Based Information Retrieval. IEEE 1997:82-90.
[90]Rui Y, Huang T, Mehrotra S, et al. Relevance feedback: A power tool for interactive content-based image retrieval[J]. IEEE Transactions on Circuits and Systems for Video Technology,1998,8(5):644-655.
[91]Lu Y, Hu C, Zhu X, et al. A unified framework for semantics and feature based relevance feedback in image retrieval systems [C]. In Proceedings of the ACM International Conference on Multimedia (MM). ACM 2000:31-37.
[92]Yang J, Liu W, Zhang H, et al. Thesaurus-aided approach for image borwsing and retrieval[C]. Pro-ceedings of International Conference on Multimedia and Expo.2001:1135-1138.
[93]张亮,施伯乐,周向东,等.发掘相关反馈日志中关联信息的图像检索方法[J].软件学报,2004,15(1)：41-48.
[94]Sabine B, Salvatore T. Visual features with semantic combination using bayesian network for a more effective image retrieval [C]. Proceedings of International Conference on Pattern Recognition (ICPR). 2008:1-4.
[95]Wu J, Lu M, Wang C. Collaborative learning between visual content and hidden semantic for image retrieval[C]. Proceedings of IEEE International Conference on Data Mining (ICDM). IEEE 2010: 1133-1138.
[96]Matthew S, Thorsten J. Learning a distance metric from relative comparisons [C]. Proceedings of the Advances in Neural Information Processing Systems (NIPS). Cambridge, MA:MIT Press,2004.
[97]Frome A, Singer Y, Malik J. Image retrieval and classification using local distance functions[C]. Pro-ceedings of the Advances in Neural Information Processing Systems (NIPS). Cambridge, MA:MIT Press,2007:417-424.
[98]Frome A, Singer Y, Fei S, et al. Learning globally-consistent local distance functions for shape-based image retrieval and classification[C]. Proceedings of IEEE International Conference on Computer Vi-sion (ICCV). IEEE 2007:1-8.
[99]Thomas D, Roberto P, Enrique V, et al. Learningweighted distances for relevance feedback in image retrieval[C]. Proceedings of International Conference on Pattern Recognition (ICPR).2008:1-4.
[100]Steven C, Liu W, Chang S. Semi-supervised distance metric learning for collaborative image re-trieval [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2008:1-8.
[101]Lee J, Jin R, Jain A. Rank-based distance metric learning:An application to image retrieval[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2008:1-8.
[102]Zhan D, Li M, Li Y, et al. Learning instance specific distances using metric propagation[C]. Pro-ceedings of the 26th International Conference on Machine Learning (ICML). NY, USA:ACM,2009: 1225-1232.
[103]Jin R, Wang S, Zhou Z. Learning a distance metric from multi-instance multi-label data[C]. Proceed-ings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2009:896-902.
[104]Tian Q, Hong P, Huang T. Update relevant image weights for content-based image retrieval using support vector machines[C]. Proceedings of International Conference on Multimedia and Expo. IEEE 2000:1199-1202.
[105]张磊,林福宗,张钹.基于支持向量机的相关反馈图像检索算法[J].清华大学学报,2002,42(1)：80-83.
[106]Lyu S. Mercer kernels for object recognition with local features [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2005:223-229.
[107]Gosselin P H, Cord M, Foliguet S P. Kernels on bags for multi-object database retrieval[C]. Proceedings of ACM International Conference on Image and Video Retrieval (CIVR). ACM 2007:226-231.
[108]Gosselin P H, Cord M, Foliguet S P. Kernels on bags of fuzzy regions for fast object retrieval[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 2007:177-180.
[109]Harchaoui Z, Bach F. Image classification with segmentation graph kernels[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2007:1-8.
[110]Lebrun J, Foliguet S, Gosselin P. Image retrieval with graph kernel on regions[C]. Proceedings of International Conference on Pattern Recognition (ICPR).2008:1-4.
[111]Grauman K, Darrell T. The pyramid match kernel:Discriminative classification with sets of image features[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2005: 1458-1465.
[112]Grauman K, Darrell T. Approximate correspondences in high dimensions[C]. Proceedings of the Ad-vances in Neural Information Processing Systems (NIPS).2007:505-512.
[113]Lazebnik S, Schmid C, Ponce J. Beyond bags of features:Spatial pyramid matching for recognizing natural scence categories[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2006:2169-2178.
[114]Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2007: 1-8.
[115]Wang J, Yang J, Yu K. et al. Locality-constrained linear coding for image classification[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2010: 3360-3367.
[116]Schietse J, Eakins J, Veltkamp R. Practice and challenges in trademark image retrieval[C]. Proceedings of ACM International Conference on Image and Video Retrieval (CIVR). New York, USA:ACM,2007: 518-524.
[117]Jain A, Vailaya A. Shape-based retrieval:A case study with trademark image database[J]. Pattern Recognition,1998,31(9):1369-1390.
[118]Wei C, Li Y, Chau W, et al. Trademark image retrieval using synthetic features for describing global shape and interior structure[J]. Pattern Recognition,2009,42(3):386-394.
[119]Unay D, Eakin A, Jasinschi R. Medical image search and retrieval using local binary patterns and klt feature points[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 2008:997-1000.
[120]Shi J, Tomasi C. Good features to track[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 1994:593-600.
[121]汪卫,王文平,汪嘉业.求一个包含点集所有点的最小圆的算法[J].软件学报,2000,11(9)：1237-1240.
[122]Wang J, Jia L, Wiederhold G. Simplicity:semantics-sensitive integrated matching for picture libraries [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2001, 23(9):947-963.
[123]Kou Y H, Chen K T, Chiang C H, et al. Query expansion for hash-based image object retrieval[C]. In Proceedings of the 17th ACM International Conference on Multimedia (MM). ACM 2009:65-74.
[124]Hsiao J H, Chen M S. Intention-focused active reranking for image object retrieval[C]. In Proceeding of the 18th ACM conference on Information and knowledge management (CIKM). ACM 2009:157-166.
[125]Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis[M]. Cambridge Univ. Press,2004.
[126]Haussler D. Convolution kernel for structure data[R]. Department of Computer Science, University of California at Santa Cruz,1999.
[127]Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification[R]. Department of Computer Science, National Taiwan University,2003.
[128]Griffin G, Holub A, Perona P. Caltech-256 object category dataset[R]. California Institute of Technol-ogy,2007.
[2]Arnold W, Worring M, Santini S, et al. Content:based image retrieval at the end of the early years[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2000,22(12):38-45.
[3]Swain M, Ballard D. Color indexing[J]. International Journal of Computer Vision (IJCV),1991, 7(1):11-32.
[4]Hafner J, Sawhney H. Efficient color histogram indexing for quadratic form distance functions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1995,17(7):729-736.
[5]Funt B, Finlayson G. Color constant color indexing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1995,17(5):522-529.
[6]Wan X, Kuo C C. A new approach to image retrieval with hierarchical color clustering[J]. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT),1998,8(5):628-643.
[7]曹莉华,柳伟,李国辉.基于多种主色调的图像检索算法研究与实现[J].计算机研究与发展,1999,36(1)：96-100.
[8]Han J, Ma K K. Fuzzy color histogram:An efficient color feature for image indexing and retrieval[C]. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE 2000:2011-2014.
[9]Han J, Ma K K. Fuzzy color histogram and its use in color image retrieval[J]. IEEE Transactions on Image Processing (TIP),2002,11(8):944-952.
[10]Strieker M, Dimai A. Spectral covariance and fuzzy regions for image indexing[J]. Machine Vision and Applications,1997,10(2):66-73.
[11]Huang J, Kumar S, Mitra M, et al. Image indexing using color correlograms[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 1997:762-768.
[12]Huang J, Ravikumar S, Mitra M, et al. Spatial color indexing and application[J]. International Journal of Computer Vision (IJCV),1999,35(3):245-268.
[13]Pass G, Zabih R, Miller J. Comparing images using color coherence vectors[C]. Proceedings of the 4th ACM International Conference on Multimedia (MM). ACM 1996:65-73.
[14]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[J].计算机辅助设计与图形学学报,2001,13(10)：912-917.
[15]Li X. Image retrieval based on perceptive weighted color blocks[J]. Pattern Recognition Letters,2003, 24(12):1935-1941.
[16]丁贵广,戴琼海,徐立文.基于兴趣点局部分布特征的图像检索方法[J].光电子.激光,2005,16(9)：1101-1106.
[17]苏小红,丁进,马培军.基于兴趣点凸包和svm加权反馈实现图像检索[J].计算机学报,2009,32(11)：2221-2228.
[18]Weijer J, Schmid C, Verbeek J, et al. Learning color names for real-world applications [J]. IEEE Transactions on Image Processing (TIP),2009,18(7):1512-1523.
[19]Aptoula E, Lefevre S. Morphological description of color images for content-based image retrieval[J]. IEEE Transactions on Image Processing (TIP),2009,18(11):2505-2517.
[20]Chen W T, Liu W C, Chen M S. Adaptive color feature extraction based on image color distributions[J]. IEEE Transactions on Image Processing (TIP),2010,19(8):2005-2016.
[21]Wengert C, Douze M, Jegou H. Bag-of-colors for improved image search[C]. Proceedings of the 19th ACM International Conference on Multimedia (MM). New York, USA:ACM,2011:1437-1440.
[22]Zhang D, Lu G. Review of shape representation and description techniques[J]. Pattern Recognition, 2004,37(1):1-19.
[23]Iivarinen J, Peura M, Srel J, et al. Comparison of combined shape descriptors for irregular objects[C]. Proceedings of the 8th British Machine Vision Conference (BMVC). Essex, Great Britain:,1997: 430-439.
[24]Vailaya A, Zhong Y, Jain A. A hierarchical system for efficient image retrieval[C]. Proceedings of International Conference on Pattern Recognition (ICPR).1996:356-360.
[25]Rangayyan R, El-Faramawy N, Desautels J, et al. Measures of acutance and shape for classification of breast tumors[J]. IEEE Transactions on Medical Imaging (TMI),1997,16(6):799-810.
[26]邬浩,潘云鹤,庄越挺,等.基于对象形状的图像查询技术[J].软件学报,1998,9(5)：344349.
[27]庄越挺.智能多媒体信息分析与检索的研究[D].博士学位论文.浙江大学,1998.
[28]Berretti S, Bimbo A, Pala P. Retrieval by shape similarity with perceptual distance and effective index-ing[J]. IEEE Transactions on Multimedia,2000,2(4):225-239.
[29]Teh C, Chin R. On image analysis by the methods of moments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1988,10(4):496-513.
[30]Liao S, Pawlak M. On image analysis by moments [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1996,18(3):254-266.
[31]Teague M. Image analysis via the general theory of moments [J].'Journal of the Optical Society of America (JOSA),1980,70(8):920-930.
[32]Zhang D, Lu G. Improving retrieval performance of zernike moment descriptor on affined shapes[C]. Proceedings of International Conference on Multimedia and Expo. Lausanne, Switzerland:,2002: 205-208.
[33]Papacostas G, Boutalis Y, Karras D, et al. A new class of zernike moments for computer vision appli-cations[J]. Information Science,2007,177(13):731-742.
[34]王冰,刘晓霞,耿国华,等.一种基于补偿法则的矩的快速算法[J1.计算机研究与发展,2003,40(7)：1042-1048.
[35]Wee C, Paramesran R. On the computational aspects of zernike moments[J]. Image and Vision Com-puting,2007,25(6):967-980.
[36]Peter K, Michael D, Horst B. Beyond pairwise shape similarity analysis [C]. Proceedings of Asian Conference on Computer Vision (ACCV). Springer 2009:655-666.
[37]Bai X, Yang X, Latecki L, et al. Learning context-sensitive shape similarity by graph transduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2010,32(5):861-874.
[38]Yang X, Latecki L. Affinity learning on a tensor product graph with applications to shape and image re-trieval[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2011:2369-2376.
[39]He D, Wang L. Texture unit, texture spectrum, and texture analysis[J]. IEEE Transactions on Geo-science and Remote Sensing,1990,28(4):509-512.
[40]Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classification based on feature distributions[J]. Pattern Recognition,1996,29(1):51-59.
[41]Ojala T, Pietikainen M, Harwood D. Multiresolution gray scale and rotation invariant texture classifi-cation with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2002,24(7):971-987.
[42]Brochard J, Khoudeir M, Augereau B. Invariant feature extraction for 3d texture analysis using the autocorrelation function[J]. Pattern Recognition,2001,22(6-7):759-768.
[43]Huang Y, Chan K. Texture decomposition by harmonics extraction from higher order statistics[J]. IEEE Transactions on Image Processing (TIP),2004,13(1):1-14.
[44]Robert A, Wang J, Laurent Y. Texture classification using windowed fourier filters[J]. IEEE Transac-tions on Pattern Analysis and Machine Intelligence (TPAMI),1997,19(2):148-153.
[45]Laine A, Fan J. Texture classification by wavelet packet signatures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1993,15(11):1186-1191.
[46]Dunn D, Higgins W, Wakeley J. Texture segmentation using 2-d gabor elementary functions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1994,16(2):130-148.
[47]Chan C, Pang G. Fabric defect detection by fourier analysis[J]. IEEE Transactions on Industry Appli-cations,2000,36(5):1267-1276.
[48]Tsai D, Heish C. Automated surface inspection for directional textures[J]. Image and Vision Comput-ing,1999,18(1):49-62.
[49]Kumar A, Pang G. Defect detection in textured materials using glabor filters[J]. IEEE Transactions on Industry Applications,2002,38(2):425-440.
[50]Bodnarova A, Bennamoun M, Latham S. Optimal glabor filters for textile flaw detection[J]. Pattern Recognition,2002,35(12):2973-2991.
[51]Tsai D, Wu S. Automated surface inspection using gabor filters[J]. International Journal of Advanced Manufacturing Technology,2000,16(7):474-482.
[52]Manjunath B, Ma W. Texture features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1996,18(8):837-842.
[53]Michael D, Faouzi K. Reversible integer-to-integer wavelet transforms for image compression:Per-formance evaluation and analysis[J]. IEEE Transactions on Image Processing (TIP),2000,9(6):1010-1024.
[54]Thyagarajan K, Tom N, Charles P. A maximum likelihood approach to texture classification using wavelet transform[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 1994:640-644.
[55]Chen J, Jain A. A structural approach to identify defects in texture images[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. IEEE 1988:29-32.
[56]Bodnarova A, Bennamoun M, Kubik K. Defect detection in textile materials based on aspects of the hvs[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. IEEE 1988: 4423-4428.
[57]Chetverikov D, Hanbury A. Finding defects in texture using regularity and local orientation [J]. Pattern Recognition,2002,35(10):2165-2180.
[58]Cross G, Jain A. Markov random field texture models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1983,5(1):25-39.
[59]Pentland A. Fractal-based description of natural scenes [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1984,6(6):661-674.
[60]Serafim A. Segmentation of natural images based on multiresolution pyramids linking of the parameters of an autoregressive rotation invariant model, application to leather defect detection[C]. Proceedings of International Conference on Pattern Recognition (ICPR).1992:41-44.
[61]Krishnamachari S, Chellappa R. Multiresolution gauss-markov random field models for texture seg- mentation[J]. IEEE Transactions on Image Processing (TIP),1997,6(2):251-267.
[62]Anh V, Maeda J, Tieng Q. Multifractal texture analysis and classification[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 1999:445-449.
[63]Wallraven C, Caputo B. Recognition with local features:the kernel recipe[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2003:257-264.
[64]Moravec H. Towards automatic visual obstacle avoidance[C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence (IJCAI). Cambridge, USA:,1977:584-584.
[65]Asada H, Brady M. The curvature primal sketch[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1986,8(1):2-14.
[66]Harris C, Stephens M. Combined corner and edge detector[C]. Proceedings of the 4th Alvey Vision Conference.1988:147-151.
[67]Horaud R, Skordas T, Veillon F. Finding geometric and relational structures in an image[C]. Proceed-ings of the 1st European Conference on Computer Vision (ECCV). Antibes, France:,1990:374-384.
[68]Rohr K. Recognizing corners by fitting parametric models[J]. International Journal of Computer Vision (IJCV),1992,9(3):213-230.
[69]Forstner W. A framework for low level feature extraction[C]. Proceedings of the 3rd European Confer-ence on Computer Vision (ECCV). Stockholm, Sweden:,1994:383-394.
[70]Lowe D. Object recognition from local scale-invariant features[J]. Proceedings of IEEE International Conference on Computer Vision (ICCV),1999,2:1150-1157.
[71]Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors [J]. International Journal of Computer Vision (IJCV),2004,60(l):63-86.
[72]Lowe D. Distinctive image features from scale-invariant keypoints[J]. International Journal of Com-puter Vision (IJCV),2004,60(2):91-110.
[73]Jiang Y, Yang J, Ngo C, et al. Representations of keypoint-based semantic concept detection:A com-prehensive study [J]. IEEE Transactions on Multimedia,2010,12(1):42-53.
[74]Sivic J, Zisserman A. Video google:A text retrieval approach to object matching in videos[C]. Pro-ceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2003:1470-1477.
[75]Nister D, Stewenius H. Scalable recognition with a vocabulary tree[C]. Proceedings of IEEE Interna-tional Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2006:2161-2168.
[76]Philbin J, Chum O, Isard M, et al. Object retrieval with large vocabularies and fast spatial matching [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2007:1-8.
[77]Tuytelaars T, Schmid C. Vector quantizing feature space with a regular lattice[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2007:1-8.
[78]Philbin J, Chum O, Isard M, et al. Lost in quantization:Improving particular object retrieval in large scale image databases[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2008:1-8.
[79]Wu Z, Ke Q, Sun J, et al. A multi-sample, multi-tree approach to bag-of-words image representation for image retrieval[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2009:1992-1999.
[80]Cao Y, Wang C, Li Z, et al. Spatial-bag-of-features[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2010:3352-3359.
[81]Zhang Y, Jia Z, Chen T. Image retrieval with geometry preserving visual phrases[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2011: 809-816.
[82]Liu J, Yang Y, Shah M. Learning semantic visual vocabularies using diffusion distance[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2009: 461-468.
[83]Zhang S, Huang Q, Hua G, et al. Building contextual visual vocabulary for large-scale image applica-tions[C]. In Proceedings of the 18th ACM International Conference on Multimedia (MM). ACM 2010: 501-510.
[84]Ji R, Yao H, Sun X, et al. Towards semantic embedding in visual vocabulary [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2010:918-925.
[85]Enser P, Sandom C. Towards a comprehensive survey of the semantic gap in visual image retrieval [C]. Proceedings of ACM International Conference on Image and Video Retrieval (CIVR). ACM 2003: 291-299.
[86]吴洪,卢汉清,马颂德.基于内容图像检索中相关反馈技术的回顾[J].计算机学报,2005,28(12)：1969-1979.
[87]Huang J, et al. Combining supervised learning with color correlograms for content-based image re-trieval[C]. In Proceedings of the ACM International Conference on Multimedia (MM). ACM 1997: 325-334.
[88]Zhong S, Zhang H, Li S, et al. Relevance feedback in content-based image retrieval:Bayesian frame-work, feature subspaces and progressive learning [J]. IEEE Transactions on Image Processing (TIP), 2003,12(8):924-937.
[89]Rui Y, Huang T, Mehrotra S, et al. A relevance feedback architecture for content-based multimedia information retrieval systems[C]. In Proceedings of IEEE Workshop on Content-Based Information Retrieval. IEEE 1997:82-90.
[90]Rui Y, Huang T, Mehrotra S, et al. Relevance feedback: A power tool for interactive content-based image retrieval[J]. IEEE Transactions on Circuits and Systems for Video Technology,1998,8(5):644-655.
[91]Lu Y, Hu C, Zhu X, et al. A unified framework for semantics and feature based relevance feedback in image retrieval systems [C]. In Proceedings of the ACM International Conference on Multimedia (MM). ACM 2000:31-37.
[92]Yang J, Liu W, Zhang H, et al. Thesaurus-aided approach for image borwsing and retrieval[C]. Pro-ceedings of International Conference on Multimedia and Expo.2001:1135-1138.
[93]张亮,施伯乐,周向东,等.发掘相关反馈日志中关联信息的图像检索方法[J].软件学报,2004,15(1)：41-48.
[94]Sabine B, Salvatore T. Visual features with semantic combination using bayesian network for a more effective image retrieval [C]. Proceedings of International Conference on Pattern Recognition (ICPR). 2008:1-4.
[95]Wu J, Lu M, Wang C. Collaborative learning between visual content and hidden semantic for image retrieval[C]. Proceedings of IEEE International Conference on Data Mining (ICDM). IEEE 2010: 1133-1138.
[96]Matthew S, Thorsten J. Learning a distance metric from relative comparisons [C]. Proceedings of the Advances in Neural Information Processing Systems (NIPS). Cambridge, MA:MIT Press,2004.
[97]Frome A, Singer Y, Malik J. Image retrieval and classification using local distance functions[C]. Pro-ceedings of the Advances in Neural Information Processing Systems (NIPS). Cambridge, MA:MIT Press,2007:417-424.
[98]Frome A, Singer Y, Fei S, et al. Learning globally-consistent local distance functions for shape-based image retrieval and classification[C]. Proceedings of IEEE International Conference on Computer Vi-sion (ICCV). IEEE 2007:1-8.
[99]Thomas D, Roberto P, Enrique V, et al. Learningweighted distances for relevance feedback in image retrieval[C]. Proceedings of International Conference on Pattern Recognition (ICPR).2008:1-4.
[100]Steven C, Liu W, Chang S. Semi-supervised distance metric learning for collaborative image re-trieval [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2008:1-8.
[101]Lee J, Jin R, Jain A. Rank-based distance metric learning:An application to image retrieval[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2008:1-8.
[102]Zhan D, Li M, Li Y, et al. Learning instance specific distances using metric propagation[C]. Pro-ceedings of the 26th International Conference on Machine Learning (ICML). NY, USA:ACM,2009: 1225-1232.
[103]Jin R, Wang S, Zhou Z. Learning a distance metric from multi-instance multi-label data[C]. Proceed-ings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2009:896-902.
[104]Tian Q, Hong P, Huang T. Update relevant image weights for content-based image retrieval using support vector machines[C]. Proceedings of International Conference on Multimedia and Expo. IEEE 2000:1199-1202.
[105]张磊,林福宗,张钹.基于支持向量机的相关反馈图像检索算法[J].清华大学学报,2002,42(1)：80-83.
[106]Lyu S. Mercer kernels for object recognition with local features [C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2005:223-229.
[107]Gosselin P H, Cord M, Foliguet S P. Kernels on bags for multi-object database retrieval[C]. Proceedings of ACM International Conference on Image and Video Retrieval (CIVR). ACM 2007:226-231.
[108]Gosselin P H, Cord M, Foliguet S P. Kernels on bags of fuzzy regions for fast object retrieval[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 2007:177-180.
[109]Harchaoui Z, Bach F. Image classification with segmentation graph kernels[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2007:1-8.
[110]Lebrun J, Foliguet S, Gosselin P. Image retrieval with graph kernel on regions[C]. Proceedings of International Conference on Pattern Recognition (ICPR).2008:1-4.
[111]Grauman K, Darrell T. The pyramid match kernel:Discriminative classification with sets of image features[C]. Proceedings of IEEE International Conference on Computer Vision (ICCV). IEEE 2005: 1458-1465.
[112]Grauman K, Darrell T. Approximate correspondences in high dimensions[C]. Proceedings of the Ad-vances in Neural Information Processing Systems (NIPS).2007:505-512.
[113]Lazebnik S, Schmid C, Ponce J. Beyond bags of features:Spatial pyramid matching for recognizing natural scence categories[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2006:2169-2178.
[114]Perronnin F, Dance C. Fisher kernels on visual vocabularies for image categorization[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2007: 1-8.
[115]Wang J, Yang J, Yu K. et al. Locality-constrained linear coding for image classification[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 2010: 3360-3367.
[116]Schietse J, Eakins J, Veltkamp R. Practice and challenges in trademark image retrieval[C]. Proceedings of ACM International Conference on Image and Video Retrieval (CIVR). New York, USA:ACM,2007: 518-524.
[117]Jain A, Vailaya A. Shape-based retrieval:A case study with trademark image database[J]. Pattern Recognition,1998,31(9):1369-1390.
[118]Wei C, Li Y, Chau W, et al. Trademark image retrieval using synthetic features for describing global shape and interior structure[J]. Pattern Recognition,2009,42(3):386-394.
[119]Unay D, Eakin A, Jasinschi R. Medical image search and retrieval using local binary patterns and klt feature points[C]. Proceedings of IEEE International Conference on Image Processing (ICIP). IEEE 2008:997-1000.
[120]Shi J, Tomasi C. Good features to track[C]. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE 1994:593-600.
[121]汪卫,王文平,汪嘉业.求一个包含点集所有点的最小圆的算法[J].软件学报,2000,11(9)：1237-1240.
[122]Wang J, Jia L, Wiederhold G. Simplicity:semantics-sensitive integrated matching for picture libraries [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),2001, 23(9):947-963.
[123]Kou Y H, Chen K T, Chiang C H, et al. Query expansion for hash-based image object retrieval[C]. In Proceedings of the 17th ACM International Conference on Multimedia (MM). ACM 2009:65-74.
[124]Hsiao J H, Chen M S. Intention-focused active reranking for image object retrieval[C]. In Proceeding of the 18th ACM conference on Information and knowledge management (CIKM). ACM 2009:157-166.
[125]Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis[M]. Cambridge Univ. Press,2004.
[126]Haussler D. Convolution kernel for structure data[R]. Department of Computer Science, University of California at Santa Cruz,1999.
[127]Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification[R]. Department of Computer Science, National Taiwan University,2003.
[128]Griffin G, Holub A, Perona P. Caltech-256 object category dataset[R]. California Institute of Technol-ogy,2007.