基于纹理的图像聚类研究
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摘要
随着互联网的蓬勃发展,基于大型图像数据库的图像检索、数据挖掘以及模式识别越来越受到人们的关注。纹理作为图像的一个非常重要的属性,与颜色、形状和布局等特征一起,在图像应用领域起着至关重要的作用。不同物体的物理表面产生不同的纹理,例如,云彩、石头、地毯等都有着各自独特的纹理特征。如何有效地利用好图像的纹理特征能够为以后更有效地区分图像打好坚实的基础。与此同时,伴随着存储和网络技术的飞速发展,图像数据库变得日益庞大,如何从海量的图像库中分析出有用的信息成为了一个挑战。聚类技术作为海量数据处理的工具,可以被用来改进基于内容的图像检索(content-based imageretrieval,CBIR)的速度和性能,也可以有效地改善图像搜索引擎的搜索结果。同时对于想快速浏览数据库的用户,图像聚类还能够用来设计方便的用户界面。
基于纹理的图像聚类主要分为纹理特征提取和聚类两个阶段。本文对这两个阶段分别进行研究,提出了相应的算法。文章的结构组织如下:本文的第1章为研究背景;第2章提出了一种基于双树复小波以及双树旋转复小波的纹理特征提取和测量算法;第3章提出了融合双树复小波和局部二值模式的纹理特征提取算法;第4章提出了一种基于谱分析的纹理图像聚类算法;第5章提出了一种采用主线和双树复小波纹理特征的掌纹识别算法;第6章给出了一个利用了掌纹纹理特征的手部特征识别系统。
本文的研究成果和创新点主要集中在以下几个方面:
1)提出了一种基于双树复小波以及双树旋转复小波的纹理提取和测量方法
研究表明人们在分析图像时,会将其分解为包含不同方向、频率的独立通道,这个和信号分析的多分辨的特点类似,因此在图像处理中,具有多分辨和局部分析能力的小波变换被大家广泛采用。虽然在小波域建立纹理特征,可以捕捉纹理的特性,但不足以很好地描述纹理结构。在实小波变换的基础上提出的双树复小波变换和旋转复小波变换,具有更多的方向选择性和近似移不变的优点。本文的第2章提出了一种基于双树复小波和旋转复小波的纹理特征提取和测量方法,首先对图像进行双树复小波和旋转复小波分解,然后对分解后的高频系数提取纹理签名,再采用Kullback-Leibler距离对提取的纹理签名进行量度。
2)提出了一种将双树复小波统计特征和局部二值模式融合的纹理提取算法
双树复小波作为一种频域技术,通过对纹理图像进行分解,可以在频域上有效地提取纹理信息。然而实验表明,可以根据图像变换的频域特性轻易构造出具有完全相同的子带统计特性而在视觉感受上完全不同的纹理图像。局部二值模式作为一种空域技术,是一种基于图像空域局部算子的纹理图像描述子,通过统计模板中心周围点的灰度值来比较不同纹理之间的差别。本文的第3章,利用将双树复小波和局部二值模式相结合,提出了一种融合的纹理特征提取算法。首先采用双树复小波对图像进行分解,提取频域纹理签名;同时用局部二值模式,提取纹理空域特征。最后,将得到的两种特征结合起来进行聚类,利用两者互补的特点,可以充分发掘纹理在频域和空域的信息特征。
3)提出了一种基于动态邻接矩阵聚类算法
因为图像数据往往存在于高维度空间中,这使得在聚类前,需要对数据做维度缩减。谱聚类由于能够有效地发掘数据之间的局部相关性,对数据空间的维度缩减有着很好的效果。谱聚类基于数据之间的相似度,并据此构建带权邻接矩阵。在聚类过程中,邻接矩阵构建得好坏直接影响到降维的效果。最常用的构建带权邻接矩阵方法,是采用高斯核来计算数据点的距离,对每个点采用近邻域法保留邻近点的边。由于全局化的尺度因子和固定的邻域个数,使得空间中数据点的分布密度不能得到很好体现,从而达不到良好的聚类效果。本文的第4章,提出了一种动态算法来从相似矩阵构建带权邻接矩阵,对于周围数据密度高的点,分配较大的尺度因子和邻域个数,有效发掘了聚类过程中的局部聚集性。
4)提出了一种图像纹理聚类新框架
传统处理图像纹理聚类的方法是,首先利用小波变换提取纹理特征,再利用主成分分析或是直接k-means对提取特征进行聚类。本文给出了一种图像纹理聚类的新框架。首先,在纹理特征提取阶段,采用双树复小波加双树旋转复小波对图像进行分解。然后对每个高频段提取直方图签名作为纹理特征。在聚类阶段,首先根据数据分布的密度来动态地计算数据点的邻接矩阵,然后再采用谱聚类进行降维。最后,对降维后的数据进行k-means聚类。
5)提出了一种基于纹理的掌纹识别检索方法
掌纹作为生物特征的一种,可以被用来有效地确定一个人的身份。掌纹识别利用掌纹的纹理信息来进行识别。本文的第5章,提出一种新的掌纹识别方法。对掌纹图像根据主线特征生成概率分布模板特征,同时采用双树复小波提取纹理细节特征,然后基于这两种特征进行分级检索。由于基于主线特征的掌纹识别利用了掌纹纹理较粗级别的显著特征,而双树复小波能有效地识别掌纹纹理的细节信息,在发掘掌纹纹理信息上,两者具有互补的特点。而且,采用概率分布的主线方法和具有近似移不变特征的双树复小波方法都对掌纹图像的旋转具有一定包容度,可以将两者结合进行分级检索。本文的第6章给出了一个掌纹识别的应用系统。
With rapid development of WWW,image retrieval,data mining and pattern recognition on large-volume image database acquire more and more attentions.As an important image feature,texture plays a critical role.Surfaces of different objects,e.g. cloud,stone and carpet etc.,exhibit different textures.Each has its own distinct texture characteristic.How to use texture efficiently can provide solid basis for detecting different images.In the meantime,as storage and network technologies grow fast, image database becomes larger and larger.People face challenges to analyze useful information from large-volume database.As one of data processing technologies, clustering is utilized to improve the speed and performance of content-based image retrieval.Clustering can also be used to improve the result of searching notably.In addition,for those who want to browse database rapidly,clustering can be used to design convenient user interface.
Texture based clustering can be divided into two phases,texture feature extraction and clustering.In this paper,we studied the two phases and proposed new algorithms accordingly.The dissertation is organized in the following way.The first chapter shows the research background.In the second chapter,an algorithm on texture feature extraction using complex wavelet tranformation is proposed.The third chapter details a texture feature extraction method that combines dual tree complex wavelet and local binary pattern.In the fourth chapter,an algorithm on constructing adjacency matrix in spectral clustering is proposed.In the fifth chapter,a palmprint recognition algorithm based on major line and dual tree complex wavelet transformation is shown.The sixth chapter demonstrates a hand-shape recognition system using multiple hand fetures, including palmprint texture.
Contributions of the dissertation are listed as follows:
1) An image texture feature extraction and measurement algorithm based on dual tree complex wavelet and rotated complex wavelet is proposed
Research shows when human eyes see images,they will decompose images into different channels including multiple directions and frequencies,which is very similar to multiresolution analysis of signal processing.For this reason,discrete wavelet transform is adopted widely in applications because of its power of multiresolution and locality analysis.However,the image texture feature captured in wavelet domain can not present texture structure well due to lack of directional information.Dual tree complex wavelet and rotated complex wavelet developed on real DWT have the properties of multiple direction selectivity and shift invariance.The second chapter proposes an image texture feature extraction algorithm based on dual tree complex wavelet and rotated complex wavelet.Images are firstly decomposed with dual tree complex wavelet and rotated complex wavelet transformations.Afterwards,texture signature is generated from the histogram statistics information of high frequency bands.Kullback-Leibler distance is applied to mesasure the distance between the textures.
2) An texture extraction algorithm that combines dual tree statistics and local binary pattern is presented
As one of frequency domain technologies,dual tree complex wavelet transformation decomposes texture images and extracts texture information on frequency domain.However,experiements show that visually distinct texture images can be constructed by subbands with same statistics characteristics according to frequency properties of image transformations.Local binary pattern is one of space domain technologies.It extracts textures by computing the difference between the gray values of the center point and the points around center point.The third chapter presents a texture feature extraction algorithm that combines dual tree complex wavelet and local binary pattem.Firstly,dual tree complex wavelet is carried out to decompose image into subbands,from which texture signatures are then computed.Meanwhile, local binary pattern is utilized to extract texture feature on the space domain.In the final clustering step,the two features are combined to calculate the distance between two different textures.Since the two texture features complement each other well,the texture information can be efficiently exploited.
3) An algorithm using dynamic factors to construct adjacency matrix is proposed
Since image data normally lies in high-dimension space,dimension reduction is needed for large-volume image database before clustering.It was proven that spectral clustering can efficiently utilize the locality relationship between image data and achieve good results in dimension reduction.Spectral clustering algorithm is based on the similarity between data points and construct weighted adjacency matrix according to the simliarites.The most commonly used approach to construct weighted adjacency matrix is Gaussian kernel.However,global scale factor and fixed neighbor number make the density of data distribution difficult to be utilized.The fourth chapter proposed an algorithm to construct weighted adjacency matrix from similarity matrix using dynamic factors.For those points located in higher density,scale factor and neighbor number become larger.By this means,local data density can be detected in clustering.
4) A new framework for image texture clustering is shown
Traditionally,image clustering can be performed as follows.Firstly,discrete wavelet transform is used to extract texture feature.Secondly PCA or k-means is carried out on the extracted features.In this dissertation,a new framework is presented. In texture feature extraction step,dual tree complex wavelet transformation and rotated complex wavelet transformation are employed to decompose images.Then,the histogram signatures are generated from high frequency subbands.In clustering step, the adjacency matrix is computed dynamically according to the density of data distribution.On the obtained adjacency matrix,spectral clustering is used to do dimensional reduction.K-means is employed finally on the dimension-reduced data.
5) A new palm recognition retrieval method on image texture is proposed
As an important biologic feature,palm can be used to determine the identity.Palm recognition is a recognition approach using palm texture information.In the fifth chapter,a novel palm recognition method is proposed.In the first step,probability distribution templates are generated based on the major lines in palms.Meanwhile, dual tree complex wavelet is used to extract detailed texture information.Then hierarchical retrieval is conducted based on the two features.Because the major line based palm recognition uses the primary information of palm texture,whereas the dual tree complex wavelet can detect the detailed information of palm texture,they can work with each other in exploiting texture information.Furthermore,the probability based major line feature and dual tree complex wavelet feature can both tolerate image rotation.The sixth chapter demonstrates an application system on palm recognition.
基于纹理的图像聚类主要分为纹理特征提取和聚类两个阶段。本文对这两个阶段分别进行研究,提出了相应的算法。文章的结构组织如下:本文的第1章为研究背景;第2章提出了一种基于双树复小波以及双树旋转复小波的纹理特征提取和测量算法;第3章提出了融合双树复小波和局部二值模式的纹理特征提取算法;第4章提出了一种基于谱分析的纹理图像聚类算法;第5章提出了一种采用主线和双树复小波纹理特征的掌纹识别算法;第6章给出了一个利用了掌纹纹理特征的手部特征识别系统。
本文的研究成果和创新点主要集中在以下几个方面:
1)提出了一种基于双树复小波以及双树旋转复小波的纹理提取和测量方法
研究表明人们在分析图像时,会将其分解为包含不同方向、频率的独立通道,这个和信号分析的多分辨的特点类似,因此在图像处理中,具有多分辨和局部分析能力的小波变换被大家广泛采用。虽然在小波域建立纹理特征,可以捕捉纹理的特性,但不足以很好地描述纹理结构。在实小波变换的基础上提出的双树复小波变换和旋转复小波变换,具有更多的方向选择性和近似移不变的优点。本文的第2章提出了一种基于双树复小波和旋转复小波的纹理特征提取和测量方法,首先对图像进行双树复小波和旋转复小波分解,然后对分解后的高频系数提取纹理签名,再采用Kullback-Leibler距离对提取的纹理签名进行量度。
2)提出了一种将双树复小波统计特征和局部二值模式融合的纹理提取算法
双树复小波作为一种频域技术,通过对纹理图像进行分解,可以在频域上有效地提取纹理信息。然而实验表明,可以根据图像变换的频域特性轻易构造出具有完全相同的子带统计特性而在视觉感受上完全不同的纹理图像。局部二值模式作为一种空域技术,是一种基于图像空域局部算子的纹理图像描述子,通过统计模板中心周围点的灰度值来比较不同纹理之间的差别。本文的第3章,利用将双树复小波和局部二值模式相结合,提出了一种融合的纹理特征提取算法。首先采用双树复小波对图像进行分解,提取频域纹理签名;同时用局部二值模式,提取纹理空域特征。最后,将得到的两种特征结合起来进行聚类,利用两者互补的特点,可以充分发掘纹理在频域和空域的信息特征。
3)提出了一种基于动态邻接矩阵聚类算法
因为图像数据往往存在于高维度空间中,这使得在聚类前,需要对数据做维度缩减。谱聚类由于能够有效地发掘数据之间的局部相关性,对数据空间的维度缩减有着很好的效果。谱聚类基于数据之间的相似度,并据此构建带权邻接矩阵。在聚类过程中,邻接矩阵构建得好坏直接影响到降维的效果。最常用的构建带权邻接矩阵方法,是采用高斯核来计算数据点的距离,对每个点采用近邻域法保留邻近点的边。由于全局化的尺度因子和固定的邻域个数,使得空间中数据点的分布密度不能得到很好体现,从而达不到良好的聚类效果。本文的第4章,提出了一种动态算法来从相似矩阵构建带权邻接矩阵,对于周围数据密度高的点,分配较大的尺度因子和邻域个数,有效发掘了聚类过程中的局部聚集性。
4)提出了一种图像纹理聚类新框架
传统处理图像纹理聚类的方法是,首先利用小波变换提取纹理特征,再利用主成分分析或是直接k-means对提取特征进行聚类。本文给出了一种图像纹理聚类的新框架。首先,在纹理特征提取阶段,采用双树复小波加双树旋转复小波对图像进行分解。然后对每个高频段提取直方图签名作为纹理特征。在聚类阶段,首先根据数据分布的密度来动态地计算数据点的邻接矩阵,然后再采用谱聚类进行降维。最后,对降维后的数据进行k-means聚类。
5)提出了一种基于纹理的掌纹识别检索方法
掌纹作为生物特征的一种,可以被用来有效地确定一个人的身份。掌纹识别利用掌纹的纹理信息来进行识别。本文的第5章,提出一种新的掌纹识别方法。对掌纹图像根据主线特征生成概率分布模板特征,同时采用双树复小波提取纹理细节特征,然后基于这两种特征进行分级检索。由于基于主线特征的掌纹识别利用了掌纹纹理较粗级别的显著特征,而双树复小波能有效地识别掌纹纹理的细节信息,在发掘掌纹纹理信息上,两者具有互补的特点。而且,采用概率分布的主线方法和具有近似移不变特征的双树复小波方法都对掌纹图像的旋转具有一定包容度,可以将两者结合进行分级检索。本文的第6章给出了一个掌纹识别的应用系统。
With rapid development of WWW,image retrieval,data mining and pattern recognition on large-volume image database acquire more and more attentions.As an important image feature,texture plays a critical role.Surfaces of different objects,e.g. cloud,stone and carpet etc.,exhibit different textures.Each has its own distinct texture characteristic.How to use texture efficiently can provide solid basis for detecting different images.In the meantime,as storage and network technologies grow fast, image database becomes larger and larger.People face challenges to analyze useful information from large-volume database.As one of data processing technologies, clustering is utilized to improve the speed and performance of content-based image retrieval.Clustering can also be used to improve the result of searching notably.In addition,for those who want to browse database rapidly,clustering can be used to design convenient user interface.
Texture based clustering can be divided into two phases,texture feature extraction and clustering.In this paper,we studied the two phases and proposed new algorithms accordingly.The dissertation is organized in the following way.The first chapter shows the research background.In the second chapter,an algorithm on texture feature extraction using complex wavelet tranformation is proposed.The third chapter details a texture feature extraction method that combines dual tree complex wavelet and local binary pattern.In the fourth chapter,an algorithm on constructing adjacency matrix in spectral clustering is proposed.In the fifth chapter,a palmprint recognition algorithm based on major line and dual tree complex wavelet transformation is shown.The sixth chapter demonstrates a hand-shape recognition system using multiple hand fetures, including palmprint texture.
Contributions of the dissertation are listed as follows:
1) An image texture feature extraction and measurement algorithm based on dual tree complex wavelet and rotated complex wavelet is proposed
Research shows when human eyes see images,they will decompose images into different channels including multiple directions and frequencies,which is very similar to multiresolution analysis of signal processing.For this reason,discrete wavelet transform is adopted widely in applications because of its power of multiresolution and locality analysis.However,the image texture feature captured in wavelet domain can not present texture structure well due to lack of directional information.Dual tree complex wavelet and rotated complex wavelet developed on real DWT have the properties of multiple direction selectivity and shift invariance.The second chapter proposes an image texture feature extraction algorithm based on dual tree complex wavelet and rotated complex wavelet.Images are firstly decomposed with dual tree complex wavelet and rotated complex wavelet transformations.Afterwards,texture signature is generated from the histogram statistics information of high frequency bands.Kullback-Leibler distance is applied to mesasure the distance between the textures.
2) An texture extraction algorithm that combines dual tree statistics and local binary pattern is presented
As one of frequency domain technologies,dual tree complex wavelet transformation decomposes texture images and extracts texture information on frequency domain.However,experiements show that visually distinct texture images can be constructed by subbands with same statistics characteristics according to frequency properties of image transformations.Local binary pattern is one of space domain technologies.It extracts textures by computing the difference between the gray values of the center point and the points around center point.The third chapter presents a texture feature extraction algorithm that combines dual tree complex wavelet and local binary pattem.Firstly,dual tree complex wavelet is carried out to decompose image into subbands,from which texture signatures are then computed.Meanwhile, local binary pattern is utilized to extract texture feature on the space domain.In the final clustering step,the two features are combined to calculate the distance between two different textures.Since the two texture features complement each other well,the texture information can be efficiently exploited.
3) An algorithm using dynamic factors to construct adjacency matrix is proposed
Since image data normally lies in high-dimension space,dimension reduction is needed for large-volume image database before clustering.It was proven that spectral clustering can efficiently utilize the locality relationship between image data and achieve good results in dimension reduction.Spectral clustering algorithm is based on the similarity between data points and construct weighted adjacency matrix according to the simliarites.The most commonly used approach to construct weighted adjacency matrix is Gaussian kernel.However,global scale factor and fixed neighbor number make the density of data distribution difficult to be utilized.The fourth chapter proposed an algorithm to construct weighted adjacency matrix from similarity matrix using dynamic factors.For those points located in higher density,scale factor and neighbor number become larger.By this means,local data density can be detected in clustering.
4) A new framework for image texture clustering is shown
Traditionally,image clustering can be performed as follows.Firstly,discrete wavelet transform is used to extract texture feature.Secondly PCA or k-means is carried out on the extracted features.In this dissertation,a new framework is presented. In texture feature extraction step,dual tree complex wavelet transformation and rotated complex wavelet transformation are employed to decompose images.Then,the histogram signatures are generated from high frequency subbands.In clustering step, the adjacency matrix is computed dynamically according to the density of data distribution.On the obtained adjacency matrix,spectral clustering is used to do dimensional reduction.K-means is employed finally on the dimension-reduced data.
5) A new palm recognition retrieval method on image texture is proposed
As an important biologic feature,palm can be used to determine the identity.Palm recognition is a recognition approach using palm texture information.In the fifth chapter,a novel palm recognition method is proposed.In the first step,probability distribution templates are generated based on the major lines in palms.Meanwhile, dual tree complex wavelet is used to extract detailed texture information.Then hierarchical retrieval is conducted based on the two features.Because the major line based palm recognition uses the primary information of palm texture,whereas the dual tree complex wavelet can detect the detailed information of palm texture,they can work with each other in exploiting texture information.Furthermore,the probability based major line feature and dual tree complex wavelet feature can both tolerate image rotation.The sixth chapter demonstrates an application system on palm recognition.
引文
1.Muerle,J.L.,Some thoughts on texture discrimination by computer.Picture Processing and Psychopictorics.1970:Academic Press.
2.Hawkins,J.K.,Textural properties for pattern recognition.Picture Processing and Psychopictorics.1969:Academic Press.
3.Haralick,R.M.,K.Shanmugam,and I.Dinstein,Textural features for image classificatiion.IEEE Transactions on Systems,Man,and Cybernetics,1973(SMC-3):610-621.
4.Cross,G.and A.Jain,Markov random field texture models.IEEE Transactions on Pattern Analysis Machine Intelligence,1983.5(1):25-39.
5.Bovik,A.C.,M.Clark,and W.S.Geisler,Multichannel texture analysis using localized spatial filters.Pattern Analysis and Machine Intelligence,IEEE Transactions on,1990.12(1):55-73.
6.Jain,A.K.and K.Karu,Learning texture discrimination masks.Pattern Analysis and Machine Intelligence,IEEE Transactions on,1996.18(2):195-205.
7.Coggins,J.M.,A framework for texture analysis based on spatial filtering,in Computer Science Department.1982,Michigan State University:East Lansing,Michigan.
8.Tamura,H.,S.Mori,and T.Yamawaki,Textural Features Corresponding to Visual Perception.Systems,Man and Cybernetics,IEEE Transactions on,1978.8(6):460-473.
9.Sklansky,J.,Image segmentation and feature extraction.IEEE Transactions on Pattern Analysis Machine Intelligence,1978.SMC(8):667-676.
10.Haralick,R.M.,Statistical and Structural Approaches to Texture,in Proceedings IEEE.1979.786-804.
11.Faugeras,O.D.and W.K.Pratt,Decorrelation methods of texture feature extraction.IEEE Transactions on Pattern Analysis Machine Intelligence, 1980(2):322-332.
12.Efros,A.A.and T.K.Leung.Texture Synthesis by Non-parametric Sampling.in IEEE International conference on Computer Vision.1999.Corfu,Greece.
13.Karu,K.,A.K.Jain,and R.M.Bolle,Is there any texture in the image? Pattern Recognition,1996.29(9):1437-1446.
14.Chen,Y.Q.,M.S.Nixon,and D.W.Thomas,Statistical Geometrical Features for Texture Classification.Pattern Recognition,1995.28(4):537-552.
15.Randen,T.and J.H.Husey,Filtering for Texture Classification:a Comparative Study.IEEE Transactions on Pattern Analysis Machine Intelligence,1999.21(4):291-310.
16.Unser,M.and M.Eden,Multiresolution Feature Extraction and Selection for Texture Segmentation.IEEE Transactions on Pattern Analysis Machine Intelligence,1989.11(7):717-728.
17.Ma,W.Y.and B.S.Manjunath,Texture-based Pattern Retrieval from Image Databases.Multimedia Tools and Applications,1996.2(1):35-51.
18.Forsyth,D.A.,Shape from Texture and Integrability,in Proceedings of IEEE International Conference on Computer Vision.2001.447-453.
19.Tuceryan,M.and A.K.Jain,"Texture Analysis",Handbook Pattern Recognition and Computer Vision,in World Scientific.1993:Singapore.235-276.
20.Rosenfeld,A.and M.Thurston,Edge and Curve Detection for Visual Scence Analysis.IEEE Transactions on Computers,1971.C-20:562-569.
21.Azencott,R.,J.P.Wang,and L.Younes,Texture Classification Using Windowed Fourier Filters.IEEE Transactions on Pattern Analysis Machine Intelligence,1997.19(2):148-153.
22.Dunn,D.,W.E.Higgins,and J.Wakeley,Texture Segmentation Using 2-D Gabor Elementary Functions.IEEE Transactions on Pattern Analysis Machine Intelligence,1994.16(2):130-149.
23.Laine,A.and J.Fan,Texture Classification by Wavelet Packet Signatures.IEEE Transactions on Pattern Analysis Machine Intelligence,1993.15(11):1186-1191.
24.Rignot,E.and R.Kwok.Extraction of Textural Features in SAR Images:Statistical Model and Sensitivity.in Proceedings of International Geoscience and Remote Sensing Symposium.1990.Washington D.C.
25.Du,L.J.Texture Segmentation of SAR Images Using Localized Spatial Filtering.in Proceedings of International Geoscience and Remote Sensing Symposium.1990.Washington D.C.
26.Christodoulou,C.I.,et al.,Texture Based Classification of Atherosclerotic Carotid Plaques.IEEE Transactions on Medical Imaging,2003.22(7):902-912.
27.Sutton,R.and E.L.Hall,Texture Mesures for Automatic Classification of Pulmonary Disease.IEEE Transactions on Computers,1972.C-21:667-676.
28.Herlidou-Meme,S.,et al.,MRI Texture Analysis on Texture Test Objects,Nomal Brain and Intracranial Tumors.Magnetic Resonance Imaging,2003.21:989-993.
29.Siew,L.H.,R.M.Hodgson,and E.J.Wood,Texture Measures for Carpet Wear Assessment.IEEE Transactions on Pattern Analysis Machine Intelligence,1988.10:92-105.
30.Jain,A.K.and F.Farrokhnia,Unsupervised Texture Segmentation Using Gabor Filters.Pattern Recognition,1991.24:1167-1186.
31.Smeulders,A.W.M.,et al.,Content-based Image Retrieval at the End of the Early Years.IEEE Transactions on Pattern Analysis Machine Intelligence,2000.22(12):1349-1380.
32.Arabic,P.,L.J.Hubert,and G.D.Soete,Clustering and Classification.1996,Singapore:Word Scientific.
33.Bradley,ES.,U.Fayyad,and C.Reina.Scaling clustering algorithms to large databases,in In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining..1998.
34.Hansen,P.and B.Jaumard,Cluster analysis and mathematical programming.Math.Programming,1997.79:191-215.
35.Symons,M.J.,Approximate clustering criteria and multivariate normal mixtures.Biometrics,1981.37:35-43.
36.Kanungo,T.,et al.,An efficient k-means clustering algorithm:analysis and implementation.IEEE Transactions on Pattern Analysis Machine Intelligence,2002.24(7):881-892.
37.Melia,M.and D.Heckerman,An experimental comparison of model-based clustering methods.Machine Learning,2001.42(1/2):143-175.
38.Sudipto,G.,R.Rajeev,and S.Kyuseok,Cure:an efficient clustering algorithm for large databases.Information Systems,2001.26(1):35-58.
39.Chavent,M.,A monothetic clustering method.Pattern Recognition Letters,1998.19(11):989-996.
40.Hartuv,E.and R.Shamir,A clustering algorithm based on graph connectivity.Information Processing Letters,2000.76:175-181.
41.Shi,J.and J.Malik,Normalized cuts and image segmentation.IEEE Transactions on Pattern Analysis Machine Intelligence,2000.22(8):888-905.
42.Malik,J.,S.Belongie,and T.Leung,Contour and texture analysis for image segmentation,in In Perceptual Organization for Artificial Vision Systems.2000:Kluwer.
43.Weiss,Y.,Segmentation using eigenvectors:a unified view,in International Conference on Computer Vision.1999.
44.Jolliffe,I.T.,Principal Component Analysis.1989,New York:Springer-Verlag.
45.Duda,R.O.,P.E.Hart,and D.G.Stork,Pattern classification,2nd edition.2000,Hoboken,NJ:Wiley Interscience.
46.Dasgupata,S.,Experiments with random projection,in In Sixteenth Conference on Uncetainty in Artificial Intelligence.2000.
47.Cohn,D.,Informed projections,in Advances in Neural Information Proceeding Systems.2002.
48.Levin,A.and A.Shashua,Principal component analysis over continuous subspaces and intersection of half-spaces,in Proceeding of the European Conference on Computer Vision.2002.
49.Xu,W.,X.Liu,and Y.Gong.Document clustering based on non-negative matrix factorization,in Proc.International Conference Research and Development in Information Retrieval.2003:ACP Press.
50.Lovasz,L.and M.Plummer,Matching theory.1986,North Holland,Budapest:Akademiai Kiado.
51.Lazebnik,S.,C.Schmid,and J.Ponce,A Sparse Texture Representation Using Local Affine Regions.IEEE Transactions on Pattern Analysis Machine Intelligence,2005.27(8):1265-1278.
52.Olkkonen,H.and J.T.Olkkonen,Half-Delay B-Spline Filter for Construction of Shift-Invariant Wavelet Transform.Circuits and Systems Ⅱ:Express Briefs,IEEE Transactions on,2007.54(7):611-615.
53.Kingsbury,N.,Complex Wavelets for Shift Inavriant Analysis and Filtering of Signals.Journal of Applied and Computational Harmonic Analysis,2001.10(3):234-253.
54.Selesnick,I.W.,The Design of Approximate Hilbert Transform Pairs of Wavelet Bases.IEEE Transactions on Signal Processing,2002.50(5):1144-1152.
55.Gopinath,R.A.,The Phaselet Transform-An Intergral Redundancy Nearly Shift-Invariant Wavelet Transform.IEEE Transactions on Signal Processing,2003.51(7):1792-1805.
56.Tay,D.B.H.ETHFB:a new class of even-length wavelet filters for Hilbert pair design,in IEEE International Symposium on Circuits and Systems.2006.
57.Selesnick,I.W.,Hilbert transform pairs of wavelet bases.IEEE Signal Processing Letter,2001.8(6):170-173.
58.Mallat,S.,A Theory for Multiresolution Signal Decomposition:the Wavelet Representation.IEEE Transactions on Pattern Analysis Machine Intelligence,1989.8(4):674-693.
59.Wouwer,G.V.and M.Vetterli,Wavelet-based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance.IEEE Transactions on Image Processing,2002.8(4):592-598.
60.Ojala,T.,M.Pietikainen,and T.Maenpaa,Multiresolution gray-scale and rotation invariant texture classification with local binary patterns.Pattern Analysis and Machine Intelligence,IEEE Transactions on,2002.24(7):971-987.
61.Ojala,T.,M.Pietikainen,and T.Maenpaa.Robust texture classification by subsets of local binary patterns,in Pattern Recognition 2000 Proceedings 15th International Conference.2000.
62.Ojala,T.,M.Pietikainen,and T.Maenpaa.Texture Classification by Multi-predicate Local Binary Pattern Operators.in Pattern Recognition 2000Proceedings 15th International Conference 2000
63.Hongliang,J.,L.Qingshan,and L.Hanqing.Face detection using improved LBP under Bayesian framework,in Image and Graphics 2004 Proceeding 3th International Conference 2004.
64.Xu,Z.,M.Pietikainen,and T.Ojala.Defect classification by texture in steel surface inspection,in Proceeding International Conference on Quality Control by Artificial Vision.1997.Burgundy,France.
65.Kyllonen,J.and M.Pietikainen.Visual inspection of parquet slabs by combining color and texture,in Proceeding IAPR Workshop on Machine Vision Applications.2000.Tokyo,Japan.
66.Silven,O.,M.Niskanen,and H.Kauppinen,Wood inspection with non-supervised clustering.Machine Vision and Application,2003.13(5-6):275-285.
67.Lew,M.,Next-generation web searches for visual content.IEEE Computer,2000.33(11):46-53.
68.Berman,A.P.and L.G.Shapiro,A flexible image database system for content-based retrieval.Computer Vision and Image Understanding,1999.75(1-2):175-195.
69.Ahonen,T.,A.Hadid,and M.Pietikainen.Face recognition with local binary pattern,in ECCV 2004 Proceedings,Lecture Notes in Computer Science.2004:Springer.
70.Heikkila,M.,M.Pietikainen,and J.Heikkila.A texture-based method for detecting moving objects,in Proceeding the 15th British Machine Vision Conference.2004.London,UK.
71.Lucieer,A.and A.Stern,Texture-based landform segmentation of LiDAR imagery.International Journal of Applied Earth Observation and Geoinformation,2005.6(3-4):261-270.
72.Lucieer,A.,Uncertainties in segmentation and their visualisation,in International Institute for Geo-information Science and Earth Observation.2004:Enschede,The Netherlands.
73.Ojala,T.,et al.,Texture discrimination with multi-dimensional distributions of signed gray level difference.Pattern Recognition,2001.34:727-739.
74.Julesz,B.,Visual Pattern Discrimination.Trans.Information Theory,1962.8:84-92.
75.Caelli,T.and B.Julesz,On perceptual analyszers underlying visual texture discrimination.Part Ⅰ:Biological Cybermetrics,1978.25:167-175.
76.Julesz,B.,E.Gilbert,and J.Victor,Visual discrimination of texture with identical third-order statistics.Biological Cybemetics,1978.31.
77.Gilbert,E.,Random colorings of a lattice on squares in the plane.SIAM J.Alogrithms of Discete Methods,1980.1:152-159.
78.Yellott,J.,Implications of triple correlation uniqueness for texture statistics and the Julesz conjecture.J.Optical Soc.Am.A,1993.10(5):777-793.
79.Randen,T.and J.H.Husoy,Filtering for texture classification:a comparative study.IEEE Transactions on Pattern Analysis Machine Intelligence,1999.21(4).
80.Ma,W.and B.Manjunath.Texture features and learning similarity,in Proc.IEEE.Conf.Computer Vision and Pattern Recognition.1996.
81.Bovic,A.C.,M.Clark,and W.S.Geisler,Multichannel texture analysis using localized spatial filters.Pattern Analysis and Machine Intelligence,IEEE Transactions on,1990.12(1):55-73.
82.Simoncelli,E.and E.Adelson,noise removal via bayesian wavelet coding,in Int'l Conf.Image Processing.1996.
83.Zhu,S.C.,Y.Wu,and D.Mumford,filters,random-fields and maximum-entropy frame:towards a unified theory for texture modeling.Int'l J.Computer Vision,1998.27(2):107-126.
84.Hadjidemetriou,E.,M.Grossberg,and S.Nayar.histogram preserving image transformations,in Proc.IEEE Conf Computer Vision and Pattern Recognition.2000.
85.Hadjidemetriou,E.,M.Grossberg,and S.Nayar.spatial information in multiresolution histgrams,in Proc.IEEE Conf Computer Vision and Pattern Recognition.2001.
86.Srivastava,A.,X.Liu,and U.Grenander,universal analytical forms for modeling image probabilities.IEEE Transactions on Pattern Analysis Machine Intelligence,2002.24(9).
87.Gluckman,J.,Visually distinct patterns with matching subband statistics.Pattern Analysis and Machine Intelligence,IEEE Transactions on,2005.27(2):252-264.
88.练秋生,李芹,and孔令富,融合圆对称轮廓波统计特征和LBP的纹理图像检索.计算机学报,2007.12:2198-2204.
89.Gordon,S.,H.Greenspan,and J.Goldberger.Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations,in Computer Vision,2003.Proceedings.Ninth IEEE International Conference on.2003.
90.Odobez,J.-M.,D.Gatica-Perez,and M.Guillemot,Spectral Structuring of Home Videos,in International Conference on Image and Video Retrieval.2003.
91.Chen,Y.,J.Z.Wang,and R.Krovetz.Content-based image retrieval by clustering,in In Proceedings of the 5th A CM SIGMM international workshop on Multimedia information retrieval.2003:ACM Press.
92.Shi,J.,et al.Image and video segmentation:the normalized cut framework,in Image Processing,1998.ICIP 98.Proceedings.1998 International Conference on.1998.
93.Jianbo,S.and J.Malik.Motion segmentation and tracking using normalized cuts.in Computer Vision,1998.Sixth International Conference on.1998.
94.Rasheed,Z.and M.Shah,A Graph Theoretic Approach for Scene Detection in Produced Videos,in Multimedia Information Retrieval Workshop.2003.
95.Mcqueen,J.Some Methods for Classification and Analysis of Multivariate Observations.in Proceeding of Fifth Berkeley Symposium on Math,Statistics and Probability.1967.
96.Jain,A.K.and R.C.Dubes,Algorithms for Clustering Data.1988,Upper Saddle River,New Jersey:Prentice-Hall Inc..
97.O'Callaghan,R.J.and D.R.Bull,Combined morphological-spectral unsupervised image segmentation.IEEE Transactions on Image Processing,2005.14(1):49-62.
98.Huet,B.and E.R.Hancock,shape recognition from large image libraries by inexact graph matching.Pattern Recognition Letters,1999.20(11-13):1259-1269.
99.Ng,A.Y.,M.I.Jordan,and Y.Weiss,On spectral clustering:Analysis and an algorithm.Advances in Neural Information Processing Systems 14.2001:MIT Press.
100.Roweis,S.and L.K.Saul,nonlinear dimensionality reduction by locally linear embedding.Science,2000.22.
101.Chung,F.,Spectral graph theory.1997,Washington:Conference Board of the Mathematical Sciences.
102.Ng,A.,M.I.Jordan,and Y.Weiss,On spectral clustering:analysis and an algorithm.Advances in Neural Information Processing Systems,2002.
103.Han,C.C.,P.C.Chang,and C.C.Hsu,Personal identification using hand geometry and palm-print,in Fourth Asian Conference on Computer Vision (ACCV).2000.
104.Lin,H.J.,et al.,Handprint Identification Using Fuzzy Inference,in The 13th IPPR Conference on Computer Vision Graphics and Image Processing.2000.
105.Zhang,D.and W.Shu,Two novel characteristics in Palmprint verification:Datum point invariance and line feature matching.Pattern Recognition,1999.32:691-702.
106.Chen,J.,C.Zhang,and G.Rong,Palmprint recognition using crease,in Proceedings,in 2001 International Conference on Image Processing.2001.
107.Kong,W.K.and D.Zhang,Palmprint texture analysis based on low-resolution images for personal authenication,in 16th International Conference on Pattern Recognition.2002.
108.Wu,X.,K.Wang,and D.Zhang,Fuzzy directional element energy feature(FDEEF) based palmprint identification,in 16th International Conference on Pattern Recognition.2002.
109.Duta,N.,A.K.Jain,and K.V.Mardia,Mactching of palmprints.Pattern Recognition Letters,2002.23:477-485.
110.You,J.,W.Li,and D.Zhang,Hierarchical pamlprint identification via multiple feature extraction.Pattern Recognition,2002.35:847-859.
111.Lu,G.,D.Zhang,and K.Wang,Palmprint recognition using eigenpalms features.Pattern Recognition Letters,2003.24(9-10):1463-1467.
112.Han,C.C.,et al.,Personal Authentication Using Palmprint Features.Pattern Recognition,2003.36(2):371-381.
113.Noh,J.S.and K.H.Rhee,Palmprint Identification Algorithm using Hu Invariant Moments and Otsu Binarization,in Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science,U.I.C.Society,Editor.2005:Washington.
114.Ito,K.,et al.,A Palmprint Recognition Algorithm using Phase-Based Image Matching,in 2006 IEEE International Conference on Image Processing,G.IEEE,Editor.2006:Atlanta.
115.李文新,张大鹏,and许卓群,基于傅立叶变换的掌纹识别方法.软件学报,2002.5(13):879-886.
116.Zuo,W.,D.Zhang,and K.Wang,Bidirectional PCA With Assembled Matrix Distance Metric for Image Recognition.IEEE Transactions on Systems,Man and Cybernetics,Part B,2006.36(4):863-872.
117.Lu,Y.,et al.,An adaptive and robust hand-based personal identification,in IEEE Mediterranean Electrotechnical Conference.2006:Benalmadena (Malaga),Spain.
118.David,G.L.,Object recognition from local scale-invariant features,in Proceedings of the International Conference on Computer Vision.1999:Corfu.
119.Li,W.,D.Zhang,and Z.Xu,Palmprint recognition based on fourier transform.Journal of Software,2002.13(5):879-886.
120.Kong,W.,D.Zhang,and W.Li,Palmprint feature extraction using 2-D Gabor filters.Pattern Recognition,2003.36:2339-2347.
121.Kingsbury,N.,Complex wavelets for shift invariant analysis and filtering of signals.Appl.Comput.Harmon.Anal.,2001.10(3):234-253.
2.Hawkins,J.K.,Textural properties for pattern recognition.Picture Processing and Psychopictorics.1969:Academic Press.
3.Haralick,R.M.,K.Shanmugam,and I.Dinstein,Textural features for image classificatiion.IEEE Transactions on Systems,Man,and Cybernetics,1973(SMC-3):610-621.
4.Cross,G.and A.Jain,Markov random field texture models.IEEE Transactions on Pattern Analysis Machine Intelligence,1983.5(1):25-39.
5.Bovik,A.C.,M.Clark,and W.S.Geisler,Multichannel texture analysis using localized spatial filters.Pattern Analysis and Machine Intelligence,IEEE Transactions on,1990.12(1):55-73.
6.Jain,A.K.and K.Karu,Learning texture discrimination masks.Pattern Analysis and Machine Intelligence,IEEE Transactions on,1996.18(2):195-205.
7.Coggins,J.M.,A framework for texture analysis based on spatial filtering,in Computer Science Department.1982,Michigan State University:East Lansing,Michigan.
8.Tamura,H.,S.Mori,and T.Yamawaki,Textural Features Corresponding to Visual Perception.Systems,Man and Cybernetics,IEEE Transactions on,1978.8(6):460-473.
9.Sklansky,J.,Image segmentation and feature extraction.IEEE Transactions on Pattern Analysis Machine Intelligence,1978.SMC(8):667-676.
10.Haralick,R.M.,Statistical and Structural Approaches to Texture,in Proceedings IEEE.1979.786-804.
11.Faugeras,O.D.and W.K.Pratt,Decorrelation methods of texture feature extraction.IEEE Transactions on Pattern Analysis Machine Intelligence, 1980(2):322-332.
12.Efros,A.A.and T.K.Leung.Texture Synthesis by Non-parametric Sampling.in IEEE International conference on Computer Vision.1999.Corfu,Greece.
13.Karu,K.,A.K.Jain,and R.M.Bolle,Is there any texture in the image? Pattern Recognition,1996.29(9):1437-1446.
14.Chen,Y.Q.,M.S.Nixon,and D.W.Thomas,Statistical Geometrical Features for Texture Classification.Pattern Recognition,1995.28(4):537-552.
15.Randen,T.and J.H.Husey,Filtering for Texture Classification:a Comparative Study.IEEE Transactions on Pattern Analysis Machine Intelligence,1999.21(4):291-310.
16.Unser,M.and M.Eden,Multiresolution Feature Extraction and Selection for Texture Segmentation.IEEE Transactions on Pattern Analysis Machine Intelligence,1989.11(7):717-728.
17.Ma,W.Y.and B.S.Manjunath,Texture-based Pattern Retrieval from Image Databases.Multimedia Tools and Applications,1996.2(1):35-51.
18.Forsyth,D.A.,Shape from Texture and Integrability,in Proceedings of IEEE International Conference on Computer Vision.2001.447-453.
19.Tuceryan,M.and A.K.Jain,"Texture Analysis",Handbook Pattern Recognition and Computer Vision,in World Scientific.1993:Singapore.235-276.
20.Rosenfeld,A.and M.Thurston,Edge and Curve Detection for Visual Scence Analysis.IEEE Transactions on Computers,1971.C-20:562-569.
21.Azencott,R.,J.P.Wang,and L.Younes,Texture Classification Using Windowed Fourier Filters.IEEE Transactions on Pattern Analysis Machine Intelligence,1997.19(2):148-153.
22.Dunn,D.,W.E.Higgins,and J.Wakeley,Texture Segmentation Using 2-D Gabor Elementary Functions.IEEE Transactions on Pattern Analysis Machine Intelligence,1994.16(2):130-149.
23.Laine,A.and J.Fan,Texture Classification by Wavelet Packet Signatures.IEEE Transactions on Pattern Analysis Machine Intelligence,1993.15(11):1186-1191.
24.Rignot,E.and R.Kwok.Extraction of Textural Features in SAR Images:Statistical Model and Sensitivity.in Proceedings of International Geoscience and Remote Sensing Symposium.1990.Washington D.C.
25.Du,L.J.Texture Segmentation of SAR Images Using Localized Spatial Filtering.in Proceedings of International Geoscience and Remote Sensing Symposium.1990.Washington D.C.
26.Christodoulou,C.I.,et al.,Texture Based Classification of Atherosclerotic Carotid Plaques.IEEE Transactions on Medical Imaging,2003.22(7):902-912.
27.Sutton,R.and E.L.Hall,Texture Mesures for Automatic Classification of Pulmonary Disease.IEEE Transactions on Computers,1972.C-21:667-676.
28.Herlidou-Meme,S.,et al.,MRI Texture Analysis on Texture Test Objects,Nomal Brain and Intracranial Tumors.Magnetic Resonance Imaging,2003.21:989-993.
29.Siew,L.H.,R.M.Hodgson,and E.J.Wood,Texture Measures for Carpet Wear Assessment.IEEE Transactions on Pattern Analysis Machine Intelligence,1988.10:92-105.
30.Jain,A.K.and F.Farrokhnia,Unsupervised Texture Segmentation Using Gabor Filters.Pattern Recognition,1991.24:1167-1186.
31.Smeulders,A.W.M.,et al.,Content-based Image Retrieval at the End of the Early Years.IEEE Transactions on Pattern Analysis Machine Intelligence,2000.22(12):1349-1380.
32.Arabic,P.,L.J.Hubert,and G.D.Soete,Clustering and Classification.1996,Singapore:Word Scientific.
33.Bradley,ES.,U.Fayyad,and C.Reina.Scaling clustering algorithms to large databases,in In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining..1998.
34.Hansen,P.and B.Jaumard,Cluster analysis and mathematical programming.Math.Programming,1997.79:191-215.
35.Symons,M.J.,Approximate clustering criteria and multivariate normal mixtures.Biometrics,1981.37:35-43.
36.Kanungo,T.,et al.,An efficient k-means clustering algorithm:analysis and implementation.IEEE Transactions on Pattern Analysis Machine Intelligence,2002.24(7):881-892.
37.Melia,M.and D.Heckerman,An experimental comparison of model-based clustering methods.Machine Learning,2001.42(1/2):143-175.
38.Sudipto,G.,R.Rajeev,and S.Kyuseok,Cure:an efficient clustering algorithm for large databases.Information Systems,2001.26(1):35-58.
39.Chavent,M.,A monothetic clustering method.Pattern Recognition Letters,1998.19(11):989-996.
40.Hartuv,E.and R.Shamir,A clustering algorithm based on graph connectivity.Information Processing Letters,2000.76:175-181.
41.Shi,J.and J.Malik,Normalized cuts and image segmentation.IEEE Transactions on Pattern Analysis Machine Intelligence,2000.22(8):888-905.
42.Malik,J.,S.Belongie,and T.Leung,Contour and texture analysis for image segmentation,in In Perceptual Organization for Artificial Vision Systems.2000:Kluwer.
43.Weiss,Y.,Segmentation using eigenvectors:a unified view,in International Conference on Computer Vision.1999.
44.Jolliffe,I.T.,Principal Component Analysis.1989,New York:Springer-Verlag.
45.Duda,R.O.,P.E.Hart,and D.G.Stork,Pattern classification,2nd edition.2000,Hoboken,NJ:Wiley Interscience.
46.Dasgupata,S.,Experiments with random projection,in In Sixteenth Conference on Uncetainty in Artificial Intelligence.2000.
47.Cohn,D.,Informed projections,in Advances in Neural Information Proceeding Systems.2002.
48.Levin,A.and A.Shashua,Principal component analysis over continuous subspaces and intersection of half-spaces,in Proceeding of the European Conference on Computer Vision.2002.
49.Xu,W.,X.Liu,and Y.Gong.Document clustering based on non-negative matrix factorization,in Proc.International Conference Research and Development in Information Retrieval.2003:ACP Press.
50.Lovasz,L.and M.Plummer,Matching theory.1986,North Holland,Budapest:Akademiai Kiado.
51.Lazebnik,S.,C.Schmid,and J.Ponce,A Sparse Texture Representation Using Local Affine Regions.IEEE Transactions on Pattern Analysis Machine Intelligence,2005.27(8):1265-1278.
52.Olkkonen,H.and J.T.Olkkonen,Half-Delay B-Spline Filter for Construction of Shift-Invariant Wavelet Transform.Circuits and Systems Ⅱ:Express Briefs,IEEE Transactions on,2007.54(7):611-615.
53.Kingsbury,N.,Complex Wavelets for Shift Inavriant Analysis and Filtering of Signals.Journal of Applied and Computational Harmonic Analysis,2001.10(3):234-253.
54.Selesnick,I.W.,The Design of Approximate Hilbert Transform Pairs of Wavelet Bases.IEEE Transactions on Signal Processing,2002.50(5):1144-1152.
55.Gopinath,R.A.,The Phaselet Transform-An Intergral Redundancy Nearly Shift-Invariant Wavelet Transform.IEEE Transactions on Signal Processing,2003.51(7):1792-1805.
56.Tay,D.B.H.ETHFB:a new class of even-length wavelet filters for Hilbert pair design,in IEEE International Symposium on Circuits and Systems.2006.
57.Selesnick,I.W.,Hilbert transform pairs of wavelet bases.IEEE Signal Processing Letter,2001.8(6):170-173.
58.Mallat,S.,A Theory for Multiresolution Signal Decomposition:the Wavelet Representation.IEEE Transactions on Pattern Analysis Machine Intelligence,1989.8(4):674-693.
59.Wouwer,G.V.and M.Vetterli,Wavelet-based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance.IEEE Transactions on Image Processing,2002.8(4):592-598.
60.Ojala,T.,M.Pietikainen,and T.Maenpaa,Multiresolution gray-scale and rotation invariant texture classification with local binary patterns.Pattern Analysis and Machine Intelligence,IEEE Transactions on,2002.24(7):971-987.
61.Ojala,T.,M.Pietikainen,and T.Maenpaa.Robust texture classification by subsets of local binary patterns,in Pattern Recognition 2000 Proceedings 15th International Conference.2000.
62.Ojala,T.,M.Pietikainen,and T.Maenpaa.Texture Classification by Multi-predicate Local Binary Pattern Operators.in Pattern Recognition 2000Proceedings 15th International Conference 2000
63.Hongliang,J.,L.Qingshan,and L.Hanqing.Face detection using improved LBP under Bayesian framework,in Image and Graphics 2004 Proceeding 3th International Conference 2004.
64.Xu,Z.,M.Pietikainen,and T.Ojala.Defect classification by texture in steel surface inspection,in Proceeding International Conference on Quality Control by Artificial Vision.1997.Burgundy,France.
65.Kyllonen,J.and M.Pietikainen.Visual inspection of parquet slabs by combining color and texture,in Proceeding IAPR Workshop on Machine Vision Applications.2000.Tokyo,Japan.
66.Silven,O.,M.Niskanen,and H.Kauppinen,Wood inspection with non-supervised clustering.Machine Vision and Application,2003.13(5-6):275-285.
67.Lew,M.,Next-generation web searches for visual content.IEEE Computer,2000.33(11):46-53.
68.Berman,A.P.and L.G.Shapiro,A flexible image database system for content-based retrieval.Computer Vision and Image Understanding,1999.75(1-2):175-195.
69.Ahonen,T.,A.Hadid,and M.Pietikainen.Face recognition with local binary pattern,in ECCV 2004 Proceedings,Lecture Notes in Computer Science.2004:Springer.
70.Heikkila,M.,M.Pietikainen,and J.Heikkila.A texture-based method for detecting moving objects,in Proceeding the 15th British Machine Vision Conference.2004.London,UK.
71.Lucieer,A.and A.Stern,Texture-based landform segmentation of LiDAR imagery.International Journal of Applied Earth Observation and Geoinformation,2005.6(3-4):261-270.
72.Lucieer,A.,Uncertainties in segmentation and their visualisation,in International Institute for Geo-information Science and Earth Observation.2004:Enschede,The Netherlands.
73.Ojala,T.,et al.,Texture discrimination with multi-dimensional distributions of signed gray level difference.Pattern Recognition,2001.34:727-739.
74.Julesz,B.,Visual Pattern Discrimination.Trans.Information Theory,1962.8:84-92.
75.Caelli,T.and B.Julesz,On perceptual analyszers underlying visual texture discrimination.Part Ⅰ:Biological Cybermetrics,1978.25:167-175.
76.Julesz,B.,E.Gilbert,and J.Victor,Visual discrimination of texture with identical third-order statistics.Biological Cybemetics,1978.31.
77.Gilbert,E.,Random colorings of a lattice on squares in the plane.SIAM J.Alogrithms of Discete Methods,1980.1:152-159.
78.Yellott,J.,Implications of triple correlation uniqueness for texture statistics and the Julesz conjecture.J.Optical Soc.Am.A,1993.10(5):777-793.
79.Randen,T.and J.H.Husoy,Filtering for texture classification:a comparative study.IEEE Transactions on Pattern Analysis Machine Intelligence,1999.21(4).
80.Ma,W.and B.Manjunath.Texture features and learning similarity,in Proc.IEEE.Conf.Computer Vision and Pattern Recognition.1996.
81.Bovic,A.C.,M.Clark,and W.S.Geisler,Multichannel texture analysis using localized spatial filters.Pattern Analysis and Machine Intelligence,IEEE Transactions on,1990.12(1):55-73.
82.Simoncelli,E.and E.Adelson,noise removal via bayesian wavelet coding,in Int'l Conf.Image Processing.1996.
83.Zhu,S.C.,Y.Wu,and D.Mumford,filters,random-fields and maximum-entropy frame:towards a unified theory for texture modeling.Int'l J.Computer Vision,1998.27(2):107-126.
84.Hadjidemetriou,E.,M.Grossberg,and S.Nayar.histogram preserving image transformations,in Proc.IEEE Conf Computer Vision and Pattern Recognition.2000.
85.Hadjidemetriou,E.,M.Grossberg,and S.Nayar.spatial information in multiresolution histgrams,in Proc.IEEE Conf Computer Vision and Pattern Recognition.2001.
86.Srivastava,A.,X.Liu,and U.Grenander,universal analytical forms for modeling image probabilities.IEEE Transactions on Pattern Analysis Machine Intelligence,2002.24(9).
87.Gluckman,J.,Visually distinct patterns with matching subband statistics.Pattern Analysis and Machine Intelligence,IEEE Transactions on,2005.27(2):252-264.
88.练秋生,李芹,and孔令富,融合圆对称轮廓波统计特征和LBP的纹理图像检索.计算机学报,2007.12:2198-2204.
89.Gordon,S.,H.Greenspan,and J.Goldberger.Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations,in Computer Vision,2003.Proceedings.Ninth IEEE International Conference on.2003.
90.Odobez,J.-M.,D.Gatica-Perez,and M.Guillemot,Spectral Structuring of Home Videos,in International Conference on Image and Video Retrieval.2003.
91.Chen,Y.,J.Z.Wang,and R.Krovetz.Content-based image retrieval by clustering,in In Proceedings of the 5th A CM SIGMM international workshop on Multimedia information retrieval.2003:ACM Press.
92.Shi,J.,et al.Image and video segmentation:the normalized cut framework,in Image Processing,1998.ICIP 98.Proceedings.1998 International Conference on.1998.
93.Jianbo,S.and J.Malik.Motion segmentation and tracking using normalized cuts.in Computer Vision,1998.Sixth International Conference on.1998.
94.Rasheed,Z.and M.Shah,A Graph Theoretic Approach for Scene Detection in Produced Videos,in Multimedia Information Retrieval Workshop.2003.
95.Mcqueen,J.Some Methods for Classification and Analysis of Multivariate Observations.in Proceeding of Fifth Berkeley Symposium on Math,Statistics and Probability.1967.
96.Jain,A.K.and R.C.Dubes,Algorithms for Clustering Data.1988,Upper Saddle River,New Jersey:Prentice-Hall Inc..
97.O'Callaghan,R.J.and D.R.Bull,Combined morphological-spectral unsupervised image segmentation.IEEE Transactions on Image Processing,2005.14(1):49-62.
98.Huet,B.and E.R.Hancock,shape recognition from large image libraries by inexact graph matching.Pattern Recognition Letters,1999.20(11-13):1259-1269.
99.Ng,A.Y.,M.I.Jordan,and Y.Weiss,On spectral clustering:Analysis and an algorithm.Advances in Neural Information Processing Systems 14.2001:MIT Press.
100.Roweis,S.and L.K.Saul,nonlinear dimensionality reduction by locally linear embedding.Science,2000.22.
101.Chung,F.,Spectral graph theory.1997,Washington:Conference Board of the Mathematical Sciences.
102.Ng,A.,M.I.Jordan,and Y.Weiss,On spectral clustering:analysis and an algorithm.Advances in Neural Information Processing Systems,2002.
103.Han,C.C.,P.C.Chang,and C.C.Hsu,Personal identification using hand geometry and palm-print,in Fourth Asian Conference on Computer Vision (ACCV).2000.
104.Lin,H.J.,et al.,Handprint Identification Using Fuzzy Inference,in The 13th IPPR Conference on Computer Vision Graphics and Image Processing.2000.
105.Zhang,D.and W.Shu,Two novel characteristics in Palmprint verification:Datum point invariance and line feature matching.Pattern Recognition,1999.32:691-702.
106.Chen,J.,C.Zhang,and G.Rong,Palmprint recognition using crease,in Proceedings,in 2001 International Conference on Image Processing.2001.
107.Kong,W.K.and D.Zhang,Palmprint texture analysis based on low-resolution images for personal authenication,in 16th International Conference on Pattern Recognition.2002.
108.Wu,X.,K.Wang,and D.Zhang,Fuzzy directional element energy feature(FDEEF) based palmprint identification,in 16th International Conference on Pattern Recognition.2002.
109.Duta,N.,A.K.Jain,and K.V.Mardia,Mactching of palmprints.Pattern Recognition Letters,2002.23:477-485.
110.You,J.,W.Li,and D.Zhang,Hierarchical pamlprint identification via multiple feature extraction.Pattern Recognition,2002.35:847-859.
111.Lu,G.,D.Zhang,and K.Wang,Palmprint recognition using eigenpalms features.Pattern Recognition Letters,2003.24(9-10):1463-1467.
112.Han,C.C.,et al.,Personal Authentication Using Palmprint Features.Pattern Recognition,2003.36(2):371-381.
113.Noh,J.S.and K.H.Rhee,Palmprint Identification Algorithm using Hu Invariant Moments and Otsu Binarization,in Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science,U.I.C.Society,Editor.2005:Washington.
114.Ito,K.,et al.,A Palmprint Recognition Algorithm using Phase-Based Image Matching,in 2006 IEEE International Conference on Image Processing,G.IEEE,Editor.2006:Atlanta.
115.李文新,张大鹏,and许卓群,基于傅立叶变换的掌纹识别方法.软件学报,2002.5(13):879-886.
116.Zuo,W.,D.Zhang,and K.Wang,Bidirectional PCA With Assembled Matrix Distance Metric for Image Recognition.IEEE Transactions on Systems,Man and Cybernetics,Part B,2006.36(4):863-872.
117.Lu,Y.,et al.,An adaptive and robust hand-based personal identification,in IEEE Mediterranean Electrotechnical Conference.2006:Benalmadena (Malaga),Spain.
118.David,G.L.,Object recognition from local scale-invariant features,in Proceedings of the International Conference on Computer Vision.1999:Corfu.
119.Li,W.,D.Zhang,and Z.Xu,Palmprint recognition based on fourier transform.Journal of Software,2002.13(5):879-886.
120.Kong,W.,D.Zhang,and W.Li,Palmprint feature extraction using 2-D Gabor filters.Pattern Recognition,2003.36:2339-2347.
121.Kingsbury,N.,Complex wavelets for shift invariant analysis and filtering of signals.Appl.Comput.Harmon.Anal.,2001.10(3):234-253.