基于语义的视频内容检索中模糊不确定性问题研究
|
摘要
近十几年来,随着计算机技术、网络通信技术和多媒体技术的飞速发展,视频信息处理的理论、方法和应用模式都有了巨大的变化,视频内容检索己成为多媒体信息技术研究和应用的活跃方向之一。视频语义中含有大量概念的、主观的成分,语义内容相当丰富,但视频图像的数字化表征,并不直观地体现其内容含义,视频语义信息的提取、理解和检索等重要环节均呈现出多样性和模糊性的特点,受限于当前图像理解技术的发展水平和对人类思维认知原理的揭示水平,基于语义的视频内容检索中不确定性仍是无法回避的关键难题,要在不确定性与确定性之间建立联系,从而使偏重主观定性的视频语义特征与偏重客观定量的视频视觉特征之间实现映射和转换仍需要面临许多挑战。
本文对基于语义视频内容检索中关键环节所涉及的典型模糊不确定性问题开展研究：首先针对视频语义特征提取和分类时先验约束条件缺失问题和噪音样本、孤立样本等对语义对象智能分类的干扰问题进行研究；然后对多线索、多特征的复杂语义分类推理规则的冲突和不协调问题,及定性推理到定量推理的转换问题进行研究；以上述研究为基础,针对视频语义匹配中多粒度、多层次语义概念间关系匹配,以及语义相关性对检索的干扰问题进行研究。在基本理论模型和应用技术方法等方面取得了如下的成果和贡献：
5.基于粗糙集属性约简的多分类模糊支持向量机
视频语义提取过程中需要使用图像低层特征信息和先验约束条件进行智能分类和识别,因视频数据具有复杂性和时空多维性的突出特点,视频图像中诸多干扰因素,会造成分类识别所需的先验约束条件缺失,使目前许多分类方法失准,另一方面分类器训练样本中既存在噪音样本、孤立样本等干扰数据,也同时存在对分类边界无重要贡献的冗余数据。针对这些问题,本文将粗糙集属性约简原理与模糊支持向量分类器相结合,在分析和研究了模糊线性可分、近似模糊线性可分和模糊非线性模糊支持向量分类机的数学特性基础上,建立相应的分类函数模型并构造隶属函数,训练集经过属性约简处理后,减弱了噪音和孤点对分类的干扰,缩短了分类器训练时间,提高了多类别分类的精度。通过仿真实验验证,对UCI数据集中8组典型测试数据集分别采用此方法与1-r-1SVM、K-SVM. CS-SVM、FSVM方法对照,训练时间平均缩短了4.4%-34%,分类精度提高0.5%～7.65%。
6.基于可能性测度和必然性测度的定量化模糊推理方法
在理解和建立视频语义概念的过程中,需要以计算机能够实现的定量形式进行领域知识匹配和不确定推理,还需要面对多线索、多特征的复杂语义分类推理规则之间的冲突和不协调问题。针对这些问题,本文从可能性测度和必然性测度来出发,对定量化不确定性推理进行数学定义和描述,并从理论上证明和推导了有关性质,使其能够对给定命题进行数值转换和推理计算。还以包含度和相似度为基础,建立推理规则间协调度的量化评价关系,为识别复杂知识规则之间的冲突,进而消除推理时的矛盾提供了一种理论方法。
7.基于WordNet的图像语义相似性度量方法；
视频图像的语义在不同粒度、不同层次的抽象,可以蕴含多种语义概念关系,这些关系主要包括：局部与整体关系、上位与下位关系、同义关系等,当使用基于关键字进行语义匹配时,关键字本身难以直接体现概念间的多种关系。针对此问题,本文的研究借助WordNet的树形概念层次结构,提出依据词汇语义概念间的关系来组织标注关键词和检索关键词的思想,因概念树中两个结点之间有且仅有一条路径,路径的长度可以作为这两个概念的语义相似性的一种度量,从而将两幅图像间的语义相似度量转换成WordNet中词汇概念之间的路径距离,使图像语义概念之间关系得以体现,进而可实现其语义关系匹配。
8.视频文档隐含语义相关性分析方法
在视频片段的各帧之间,在语义结构的各层次之间均广泛存在着各种相关性,当使用向量空间形式表示和处理语义概念时,会产生同义与多义现象干扰检索结果的不利影响,其原因是表达语义概念的词汇数据之间存在着向量相关性,需要消除冗余的相关性,并要保留核心的语义内容。针对此问题,本文对隐含语义分析方法加以改进和扩展,提出视频特征词典空间的构建方法,以视频特征词典为基础,建立反映视频内容结构特征的视频文档集合矩阵,通过消减视频文档集合矩阵中的隐含相关向量,保留视频内容核心结构特征值,以达到消除相关性干扰的目的。经过TERCVID数据集进行仿真实验验证,对典型的17个语义项用本方法与对照的K-NN算法进行比较,本方法对其中70.59%的语义项检索效果优于对照算法,对17.65%的语义项检索效果与对照算法基本持平,取得了较好的效果。
综上所述,本文以基于语义的视频内容检索为研究背景,对视频语义提取、领域知识推理、语义概念匹配等关键环节中所面临的几个典型模糊不确定性问题展开研究,在基本理论和应用技术方面为视频语义检索拓展了值得进一步深入探索和发展的途径,具有重要的理论意义和应用价值。
Over the last decade, with the development at full speed of computer technology, network communication and multimedia technology, there are enormous changes for visual information processing theory, method, and application mode. Content-base video retrieval has become one active area of multimedia technical research and application. The semantic of video content contains a large number of conceptual and subjective components, but the digitization modality of the video image does not reflect its meaning of content concretely and ocularly. Some essential processing procedure, such as video semantic information extraction, understanding and retrieval, is to show diversity and fuzziness characteristics. Uncertainty is still a key difficult problem that cannot be avoided in the content-based video semantic retrieval. While to build the relationship between uncertainty and certainty, and to realize the mapping between video semantic features and visual features, researcher still has to face many challenges.
The main achievements of this dissertation are summarized as follows:
(1) Multi-category fuzzy Support Vector Machines method based on the rough set attribute reduction
Video semantic extraction process requires the use of low-level image feature and a priori constraints for intelligent classification and identification. Various interference factors in video images will lead to inaccurate classification. On the other hand, there were not only the noise samples and isolated samples in classifier training sets, there was also redundant data that is useless for identifying classification boundary. To solve this problem, based on the analysis and study mathematical characteristics of fuzzy linearly classification, nearly fuzzy linear classification and fuzzy nonlinear classification fuzzy support vector classification, classification of function model is established and membership function is constructed. The training set which is processed by attribute reduction, reduce the interference of the noise and isolated points on the classification, reduce the classifier training time and improve the classification accuracy. Experiments show that training time is reduced by an average of4.4%-34%, and that the classification accuracy increased by0.5%～7.65%
(2) The possibility measure and necessity measure based on quantitative method of fuzzy reasoning
During the process of understanding and establishment video semantic concept, conflicts and inconsistencies between complicated reasoning rules have to face and solve. These rules are more clues and more complex features of the semantic classification. To solve this problem this dissertation is to define and describe the quantitative of uncertainty reasoning, to prove and deduce theoretically related properties.
(3) WordNet-based image semantic similarity measure
Video image semantic in different particle size, different levels of abstraction, can contain various kinds of semantic relations. These relationships mainly include local and the overall relationship, upper and lower level relations, synonymous relation, etc. When using keyword-based semantic matching, keyword itself is difficult to be directly reflect the various relations between concepts. To solve this problem, This dissertation presents the method based on the relationship between lexical semantics to organize annotation keywords and retrieve keywords. Because there is only one path between two nodes in the semantic concept tree, path length between two concepts can be used as a measure of semantic similarity. Therefore, the measure of semantic similarity between two images can convert the measure of path length between two concepts in WordNet tree.
(4) The video latent semantic correlation analysis method
Between video clips, between the various levels of semantic structure, there are varieties of correlations. This may be obstruct the retrieval result, so it need to eliminate the redundant correlation and to retain the essential semantic content.
To solve this problem, this dissertation presents the method that builds the space of video feature dictionary. Using video feature dictionary to describe the structure of video content, then latent correlation vectors were removed, and the essential feature of video contents were retained. As a result, correlation interferences were eliminated. Experiments show that this method was70.59%better than the Contrast algorithm for the retrieval result of semantic items, and was17.65%equal to the Contrast algorithm for them.
本文对基于语义视频内容检索中关键环节所涉及的典型模糊不确定性问题开展研究：首先针对视频语义特征提取和分类时先验约束条件缺失问题和噪音样本、孤立样本等对语义对象智能分类的干扰问题进行研究；然后对多线索、多特征的复杂语义分类推理规则的冲突和不协调问题,及定性推理到定量推理的转换问题进行研究；以上述研究为基础,针对视频语义匹配中多粒度、多层次语义概念间关系匹配,以及语义相关性对检索的干扰问题进行研究。在基本理论模型和应用技术方法等方面取得了如下的成果和贡献：
5.基于粗糙集属性约简的多分类模糊支持向量机
视频语义提取过程中需要使用图像低层特征信息和先验约束条件进行智能分类和识别,因视频数据具有复杂性和时空多维性的突出特点,视频图像中诸多干扰因素,会造成分类识别所需的先验约束条件缺失,使目前许多分类方法失准,另一方面分类器训练样本中既存在噪音样本、孤立样本等干扰数据,也同时存在对分类边界无重要贡献的冗余数据。针对这些问题,本文将粗糙集属性约简原理与模糊支持向量分类器相结合,在分析和研究了模糊线性可分、近似模糊线性可分和模糊非线性模糊支持向量分类机的数学特性基础上,建立相应的分类函数模型并构造隶属函数,训练集经过属性约简处理后,减弱了噪音和孤点对分类的干扰,缩短了分类器训练时间,提高了多类别分类的精度。通过仿真实验验证,对UCI数据集中8组典型测试数据集分别采用此方法与1-r-1SVM、K-SVM. CS-SVM、FSVM方法对照,训练时间平均缩短了4.4%-34%,分类精度提高0.5%～7.65%。
6.基于可能性测度和必然性测度的定量化模糊推理方法
在理解和建立视频语义概念的过程中,需要以计算机能够实现的定量形式进行领域知识匹配和不确定推理,还需要面对多线索、多特征的复杂语义分类推理规则之间的冲突和不协调问题。针对这些问题,本文从可能性测度和必然性测度来出发,对定量化不确定性推理进行数学定义和描述,并从理论上证明和推导了有关性质,使其能够对给定命题进行数值转换和推理计算。还以包含度和相似度为基础,建立推理规则间协调度的量化评价关系,为识别复杂知识规则之间的冲突,进而消除推理时的矛盾提供了一种理论方法。
7.基于WordNet的图像语义相似性度量方法；
视频图像的语义在不同粒度、不同层次的抽象,可以蕴含多种语义概念关系,这些关系主要包括：局部与整体关系、上位与下位关系、同义关系等,当使用基于关键字进行语义匹配时,关键字本身难以直接体现概念间的多种关系。针对此问题,本文的研究借助WordNet的树形概念层次结构,提出依据词汇语义概念间的关系来组织标注关键词和检索关键词的思想,因概念树中两个结点之间有且仅有一条路径,路径的长度可以作为这两个概念的语义相似性的一种度量,从而将两幅图像间的语义相似度量转换成WordNet中词汇概念之间的路径距离,使图像语义概念之间关系得以体现,进而可实现其语义关系匹配。
8.视频文档隐含语义相关性分析方法
在视频片段的各帧之间,在语义结构的各层次之间均广泛存在着各种相关性,当使用向量空间形式表示和处理语义概念时,会产生同义与多义现象干扰检索结果的不利影响,其原因是表达语义概念的词汇数据之间存在着向量相关性,需要消除冗余的相关性,并要保留核心的语义内容。针对此问题,本文对隐含语义分析方法加以改进和扩展,提出视频特征词典空间的构建方法,以视频特征词典为基础,建立反映视频内容结构特征的视频文档集合矩阵,通过消减视频文档集合矩阵中的隐含相关向量,保留视频内容核心结构特征值,以达到消除相关性干扰的目的。经过TERCVID数据集进行仿真实验验证,对典型的17个语义项用本方法与对照的K-NN算法进行比较,本方法对其中70.59%的语义项检索效果优于对照算法,对17.65%的语义项检索效果与对照算法基本持平,取得了较好的效果。
综上所述,本文以基于语义的视频内容检索为研究背景,对视频语义提取、领域知识推理、语义概念匹配等关键环节中所面临的几个典型模糊不确定性问题展开研究,在基本理论和应用技术方面为视频语义检索拓展了值得进一步深入探索和发展的途径,具有重要的理论意义和应用价值。
Over the last decade, with the development at full speed of computer technology, network communication and multimedia technology, there are enormous changes for visual information processing theory, method, and application mode. Content-base video retrieval has become one active area of multimedia technical research and application. The semantic of video content contains a large number of conceptual and subjective components, but the digitization modality of the video image does not reflect its meaning of content concretely and ocularly. Some essential processing procedure, such as video semantic information extraction, understanding and retrieval, is to show diversity and fuzziness characteristics. Uncertainty is still a key difficult problem that cannot be avoided in the content-based video semantic retrieval. While to build the relationship between uncertainty and certainty, and to realize the mapping between video semantic features and visual features, researcher still has to face many challenges.
The main achievements of this dissertation are summarized as follows:
(1) Multi-category fuzzy Support Vector Machines method based on the rough set attribute reduction
Video semantic extraction process requires the use of low-level image feature and a priori constraints for intelligent classification and identification. Various interference factors in video images will lead to inaccurate classification. On the other hand, there were not only the noise samples and isolated samples in classifier training sets, there was also redundant data that is useless for identifying classification boundary. To solve this problem, based on the analysis and study mathematical characteristics of fuzzy linearly classification, nearly fuzzy linear classification and fuzzy nonlinear classification fuzzy support vector classification, classification of function model is established and membership function is constructed. The training set which is processed by attribute reduction, reduce the interference of the noise and isolated points on the classification, reduce the classifier training time and improve the classification accuracy. Experiments show that training time is reduced by an average of4.4%-34%, and that the classification accuracy increased by0.5%～7.65%
(2) The possibility measure and necessity measure based on quantitative method of fuzzy reasoning
During the process of understanding and establishment video semantic concept, conflicts and inconsistencies between complicated reasoning rules have to face and solve. These rules are more clues and more complex features of the semantic classification. To solve this problem this dissertation is to define and describe the quantitative of uncertainty reasoning, to prove and deduce theoretically related properties.
(3) WordNet-based image semantic similarity measure
Video image semantic in different particle size, different levels of abstraction, can contain various kinds of semantic relations. These relationships mainly include local and the overall relationship, upper and lower level relations, synonymous relation, etc. When using keyword-based semantic matching, keyword itself is difficult to be directly reflect the various relations between concepts. To solve this problem, This dissertation presents the method based on the relationship between lexical semantics to organize annotation keywords and retrieve keywords. Because there is only one path between two nodes in the semantic concept tree, path length between two concepts can be used as a measure of semantic similarity. Therefore, the measure of semantic similarity between two images can convert the measure of path length between two concepts in WordNet tree.
(4) The video latent semantic correlation analysis method
Between video clips, between the various levels of semantic structure, there are varieties of correlations. This may be obstruct the retrieval result, so it need to eliminate the redundant correlation and to retain the essential semantic content.
To solve this problem, this dissertation presents the method that builds the space of video feature dictionary. Using video feature dictionary to describe the structure of video content, then latent correlation vectors were removed, and the essential feature of video contents were retained. As a result, correlation interferences were eliminated. Experiments show that this method was70.59%better than the Contrast algorithm for the retrieval result of semantic items, and was17.65%equal to the Contrast algorithm for them.
引文
[1]魏维,游静,刘凤玉et a1.语义视频检索综述[J].计算机科学,2006,33(02)：1-8.
[2]朱蓉.基于语义信息的图像理解关键问题研究[J].计算机应用研究,2009(04)：1234-1240.
[3]R.Jain. Proc. Us Nsf Workshop Visual Information Management Systems[C]. Redwood,CA,USA,1992.
[4]Arnold W.M.Smeulders, Marcel Wbrring, Simones Santini et al. Content-Based Image Retrieval at the End of the Early Years[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12).
[5]章毓晋.中国图像工程：2004[J]．中国图象图形学报,2005(05)：5-28.
[6]章毓晋.中国图像工程：2005[J]．中国图象图形学报,2006(05)：7-29.
[7]章毓晋.中国图像工程：2006[J]．中国图象图形学报,2007(05)：6-28.
[8]章毓晋.中国图像工程：2007[J]．中国图象图形学报,2008(05)：6-33.
[9]章毓晋.中国图像工程：2008[J]．中国图象图形学报,2009(05)：63-91.
[10]M. Flickner, H. Sawhney, W. Niblack et al. Query by Image and Video Content:The Qbic System[J]. Computer,1995,28(9):23-32.
[11]Rui Y, Huang T S, Mehrotra S. Content-Based Image Retrieval with Relevance Feedback in Mars[J]. Image Processing,1997(2):26-29.
[12]Zhuang Y, Liu X, Pan Y. Webscope-Cbvr:A Customized Content-Based Search Engine for Video on Www[C]. Proceeding of IS&T and SPIE Image and Video Communication and Processing,2000.
[13]Alan F. Smeaton, Peter Wilkins, Marcel Worring et al. Content-Based Video Retrieval:Three Example Systems from Trecvid[J]. International Journal of Imaging Systems and Technology, 2008,18(2-3):195-201.
[14]Peter Wilkins, Alan F. Smeaton, Noel E. O'Connor et al. K-Space Interactive Search[C]. Proceedings of the 2008 ACM International Conference on Content-Based Image and Video Retrieval, Niagara Falls, Canada,2008,555-556.
[15]Ork de Rooij, Cees G.M. Snoek, Marcel Worring. Mediamill:Fast and Effective Video Search Using the Forkbrowser[C]. Proceedings of the 2008 international conference on Content-based image and video retrieval, Niagara Falls, Canada,2008,561-562.
[16]Shi-Yong Neo, Huanbo Luan, Yantao Zheng et al. Visiongo:Bridging Users and Multimedia Video Retrieval[C]. Proceedings of the 2008 international conference on Content-based image and video retrieval, Niagara Falls, Canada,2008,559-560.
[17]贺莉娜.视频语语义特征提取的研究[D]．硕士论文,北京交通大学,2008.
[18]Wandell, B. A. The Synthesis and Analysis of Color Image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(1):2-13.
[19]Swain, M. J., D.H Ballard. Color Indexing[J]. International Journal of Computer Vision,1991, 7(1):11-32.
[20]Stricken M., M. Orengo. Similarity of Color[J]. SPIE Storage and Retrieval for Image and Viedeo Databases Ⅲ,1995,2185:381-392.
[21]黄朝兵,余胜生,周敬利.基于多邻域统计直方图的彩色图像检索[J]．小型微型计算机系统,2005,26(6)：1061-1064.
[22]J. Huang, S. R. Kumar. Image Indexing Using Color Correlogram[C]. IEEE Conference of ter Vision Pattern Recognition (CVPR 97), San.Juan, Puerto Rico,1997,762-768.
[23]Zachary J M. An Information Theoretic Approach to Content Based Image Retrieval[D]. Phd.Thesis, Louisiana State University and Agricultural and Mechanical College,2000.
[24]Jun ding Sun, Ximin Zhang, Jiangtao Cui et al. Image Retrieval Based on Color Distribution Entropy[J]. Pattern Recognition Letters,2006,27:1122-1126.
[25]Fuh C. S., S. W.Cho. Hierarchical Color Image Region Segmentation for Content-Based Image Retrieval System[J]. IEEE Transactions On Image Processing,2000,9(1):156-163.
[26]史迎春.基于内容的视频检索语义提取若干问题研究[D].博士论文,控制科学气工程,南京理工大学,南京,2005.5.
[27]Tamura, H., S. Mori. Texture Features Corresponding to Visual Perception[J]. IEEE Transactions on System,Man and Cybernetics,1978,8(8):460-473.
[28]Ma, W.-Y, B. S. Manjunath. A Comparison of Wavelet Transform Features for Texture Image Annotation[C]. Proceedings of IEEE International Conference on Image Processing (ICIP 1995), Washington DC, USA,1995,256-259.
[29]Pirrone R., M. L. Cascia. Texture Classification for Content-Based Image Retrieval[C]. International Conference on Image Analysis and Processing (ICIAP), Palermo, Italy,2001, 398-403.
[30]Hu, M.-K. Visual Pattern Recognition by Moment Invariants[J]. IEEE Transactions Information Theory,1962,8(2):179-187.
[31]曾璞.面向语义提取的图像分类关键技术研究[D].博士论文,控制科学与工程,国防科学技术大学,合肥,2009.
[32]H.Moravec. Towards Automatic Visual Obstacle Avoidance[C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence,1977,584-590.
[33]Crowley J. A Representation for Visual Information[D]. Ph.D., Mellon University, Carnegie, 1981.
[34]Tony Lindeberg. Feature Detection with Automatic Scale Selection[J]. International Journal of Computer Vision,1998,30(2):79-116.
[35]David G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision,2004,60:91-110.
[36]Krystian Mikolajczyk, Cordelia Schmid. Scale & Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision,2004,60:63-86.
[37]K. Mikolajczyk, C. Schmid. An Affine Invariant Interest Point Detector[C]. Proceedings of the 7th European Conference on Computer Vision (ECCV 2002), Copenhagen, Denmark,2002, 128-142.
[38]T. Kadir, M. Brady. Saliency, Scale and Image Description [J]. International Journal of Computer Vision,2001,45(2):83-105.
[39]Tinne Tuytelaars, Luc Van Gool. Matching Widely Separated Views Based on Affine Invariant Regions[J]. International Journal of Computer Vision,2004,59:61-85.
[40]J. Matas, O. Chum, M. Urban et al. Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions[J]. Image Vision Comput,2004,22(10 SPEC. ISS.):761-767.
[41]蔡红苹,雷琳,陈涛et aI.一种通用的仿射不变特征区域提取方法[J].电子学报,2008,36(4)：672-278.
[42]K. Mikolajczyk, T. Tuytelaars, C. Schmid et al. A Comparison of Affine Region Detectors[J]. International Journal of Computer Vision,2005,65:43-72.
[43]W. T. Freeman, E. H. Adelson. The Design and Use of Steerable Filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(9):891-906.
[44]J.G. Daugman. Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters[J]. Journal of the Optical Society of America A,1985,2(7):1160-1169.
[45]F. Schaffalitzky, A. Zisserman. Multi-View Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"[J]. Computer Vision—ECCV 2002,2002:414-431.
[46]T. Leung, J. Malik. Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons[J]. International Journal of Computer Vision,2001,43(1):29-44.
[47]K. Mikolajczyk, C. Schmid. A Performance Evaluation of Local Descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[48]N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005(CVPR 2005),2005,886-893
[49]J. van de Weijer, T. Gevers, A. D. Bagdanov. Boosting Color Saliency in Image Feature Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1): 150-156.
[50]A. Bosch, A. Zisserman, X. Munoz. Representing Shape with a Spatial Pyramid Kernel[C]. ACM,2007,401-408.
[51]K.E.A. van de Sande, T. Gevers, C.G.M. Snoek. A Comparison of Color Features for Visual Concept Classification[J]. ACM,2008,141-150.
[52]M. Stark, B. Schiele. How Good Are Local Features for Classes of Geometric Objects[C]. IEEE,2007,1-8.
[53]K. Mikolajczyk, B. Leibe, B. Schiele. Local Features for Object Class Recognaition[J].2005.
[54]Eakins, J. P., M. E. Graham. Content Based Image Retrieval.[J]. A report to the JISC Technology Application Programme,1999.
[55]D. Hong, J. K. Wu. Refining Image Retrieval Based on Context Driven Methods[C]. IS&SPIE I1 th Symposium on Electronic Imaging, San Jose,CA, USA,1999,581-592.
[56]A Jaimes, S.F. Chan. Model-Based Classification of Visual Information for Content-Based Retrieval[C]. Proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases VII, San Jose, CA,1999,402-414.
[57]王忠锋,孙正兴，王箭.语义图像检索研究进展[J].计算机研究与发展，2002、39(05)：511.523.
[58]高永英,章毓晋．基于多级描述模型的渐进式图像内容理解[J]．电子学报,2001,29(10)：1376-1380.
[59]Yu Wang, ChunXiao Xing, Lizhu Zhou. Video Semantic Models:Survey and Evaluation[J]. International Journal of Computer Science and Network Security(IJCSNS),2006,8(2A): 10-20.
[60]Luo J, A. E. Savakis. A Bayesian Network-Based Framework for Semantic Image Understanding[J]. Pattern Recognition,2005,38(919-934).
[61]Aksoy S., K. Koperski. Learning Bayesian Classifiers for Scene Classification with a Visual Grammar[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(3):581-589.
[62]王上飞,际恩红.基于支持向量机的图像情感注释和检索算法的研究[J]．模式识别 和人工智能,2004,17(1)：27-33.
[63]Yu Tao Han, Xiao Jun Qi. A Complementary Svms-Based Image Annotation System[C]. Processing of IEEE International Conference on Image 2005(ICIP 2005), Genova, Italy, 2005,1185-1188.
[64]K. S. Goh, E. Y. Chang, B. Li. Using One-Class and Two-Class Svms for Multiclass Image Annotation[J]. IEEE Transactions on Knowledge and Data Engineering,2005,17(10): 1333-1346.
[65]H Feng, T S Chua. A Bootstrapping Approach to Annotating Large Image Collection[C]. Workshop on Multimedia Information Retrieval in ACM Multimedia, Berkeley,CA,USA 2003.
[66]C P Town, D Sinclair. Content-Based Image Retrieval Using Semantic Visual Categories[J]. Society for Manufacturing Engineers,Technical Report MV01-211,2001.
[67]Liu W. Y., Y. Sun. Mialbum-a System for Photo Management Using the Simi-Automatic Image Annotation Approach [C]. Proceedings of ACM Multimedia, Los Angeles.USA,2000, 479-480.
[68]朱兴全,张宏江.Ifind：一个结合语义和视觉特征的图像相关反馈检索系统[J].计算机学报,2002,25(7)：681-688.
[69]He Xiaofei, KING O. Learning a Semantic Space from User's Relevance Feedback for Image Retrieval[J]. IEEE Transactions on Circuits and Systems for Video Technology,2003,13(1): 39-48.
[70]Han Jun Wei, Ngan King N, Li MingJing et al. A Memory Learning Framework for Effective Image Retrieval[J]. IEEE Transactions on Image Processing,2005,14(4):511-524.
[71]Srihari R. K., Z. Zhang. Intelligent Indexing and Semantic Retrieval of Multimodal Documents[J]. Information Retrieval,2000,2(2):245-275.
[72]Shen H. T., B. C. Ooi. Giving Meanings to Www Images[C]. Proceedings of the 8th ACM International Conference on Multimedia (ACM MM), Los Angeles, California,2000,39-47.
[73]张华,张淼.基于外部信息源的 WWW 图像语义提取研究[J].计算机科学,2006，33(4)：211-214.
[74]C Fellbaum.Wordnet:An Electronic Lexical Database[M], Cambridge,MA:MIT Press,1998.
[75]Luca Lombardi, Alfredo Petrosino. Distributed Recursive Learning for Shape Recognition through Multiscale Trees[J]. Image and Vision Computing,2007,25:240-247.
[76]高永英,章毓晋.基于多级描述模型的渐进式图像内容理解[J].电子学报,2001，29(10)：1376-1380.
[77]T. C. Chou, S. C. Cheng. Design and Implementation of a Semantic Image Classification and Retrieval of Organizational Memory Information Systems Using Analytical Hierarchy Process[J]. Omega-International Journal of Management Science,2006,34(2):125-134.
[78]Shyi-Chyi Cheng, Tzu-Chuan Chou, Chao-Lung Yang et al. A Semantic Learning for Content-Based Image Retrieval Using Analytical Hierarchy Process[J]. Expert Systems with Applications,2005,28(3):495-505.
[79]万华林,Morshed U.Chowdhury基于支持向量机的图像语义分类[J].软件学报,2003,14(11)：1891-1899.
[80]Boutell M., J. Luo. Bayesian Fusion of Camera Metadata Cues in Semantic Scene Classification[C]. Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'04), NY, USA,2004,623-630.
[81]Borja Calvo, Pedro Larranaga, Jose A. Lozano. Learning Bayesian Classifiers from Positive and Unlabeled Examples[J]. Pattern Recognition Letters,2007,28(16):2375-2384.
[82]粱竞敏.集成学习svm在图像检索中的应用[J].计算机工程与应用，2009(18)：182-184+230.
[83]G. Sheikholeslami, W. Chang, Zhang Aidong. Semquery:Semantic Clustering and Querying on Heterogeneous Features for Visual Data[J]. IEEE Transactions on Knowledge and Data Engineering,2002,14(5):988-1002.
[84]J. Goldberger, S. Gordon, H. Greenspan. Unsupervised Image-Set Clustering Using an Information Theoretic Framework [J]. IEEE Transactions on Image Processing 2006,15(2): 449-458.
[85]K. Barnard, D. Forsyth. Learning the Semantics of Words and Pictures[C]. Proceedings of the 8th International Conference on Computer Vision, Vancouver, BC, United States,2001, 408-415.
[86]James Z Wang, Jia Li. Learning-Based Linguistic Indexing of Pictures with 2-D Mhmms[C]. Proceedings of the ACM International Multimedia Conference and Exhibition, Association for Computing Machinery,2002,436-445.
[87]David M Blei, Andrew Y Ng, Michael I Jordan. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research,2003,3(4-5):993-1022.
[88]J. Jeon, V. Lavrenko, R. Manmatha. Automatic Image Annotation and Retrieval Using Cross-Media Relevance Models[C]. Proceedings of the 23th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 2003), Toronto, Ont., Canada,2003,119-126.
[89]J Jeon, R Manmatha. Using Maximum Entropy for Automatic Image Annotation[C]. Proceeding of the 3rd International Conference on Image and Video Retrieval (CIVR 2004), Dublin, IRELAND,2004,24-32.
[90]Rong Jin, Joyce Y. Chai, Luo Si. Effective Automatic Image Annotation Via a Coherent Language Model and Active Learning[C]. Proceedings of the 12th ACM International Conference on Multimedia (ACMM 2004), New York, NY, United States,2004,892-899.
[91]P Duygulu, K Barnard, J de Freitas et al. Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary[M]. Computer Vision-Eccv 2002, Lecture Notes in Computer Science A. Heyden, G. Sparr, M. Nielsen et al., eds., pp.349-354: Springer Berlin/Heidelberg,2006.
[92]J. Z. Wang, Li Jia, G. Wiederhold. Simplcity:Semantics-Sensitive Integrated Matching for Picture Libraries[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001, 23(9):947-963.
[93]D. Brahmi, D. Ziou. Improving Cbir Systems by Integrating Semantic Features[C]. Proceedings of the 1 st Canadian Conference on Computer and Robot Vision (CRV'04) London, Ontario, Canada,2004,233-240.
[94]Yang C., M. Dong, et al. Learning the Semantics in Image Retrieval-a Natural Language Processing Approach[C]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Patern Recgnition Workshops (CVPRW'04),2004,137-143.
[95]李清勇,施智平et aI．基于纹理语义特征的图像检索研究[J]．计算机学报,2006,29(1)：116-123.
[96]Ritendra Datta, Jia Li, James Z. Wang. Content-Based Image Retrieval-Approaches and Trends of the New Age[C]. Proceedings of the 7th International Workshop on Multimedia Information Retrieval,In Conjunction with ACM International conference on Multimedia Systems, Singapore,2005,253-262.
[97]Yin X., M. Li, et al. Semantic Image Clustering Using Relevance Feedback[C]. IEEE International Symposium on Circuits and Systems (ISCAS'03),2003,904-907.
[98]王崇骏,杨育彬,陈世福.基于高层语义的图像检索算法[J].软件学报,2004，15(10)：1461¨1469.
[99]Haas M, Rijsdam J, Lew M. Relevance Feedback:Perceptual Learning and Retrieval in Bio-Computing, Photos, and Video[C]. Processings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieva, New York,2004,151-156.
[100]万小军杨建武.基于语义拓扑网的反馈学习技术[J].计算机工程,2005,31(1)：6-8.
[101]胡莹,王昱,丁明跃.一种改进的svm相关反馈图像检索方法[J].计算机应用研究,2005,22(1)：251¨254.
[102]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.
[103]张会章,张利霞,郭雷.图像理解[J].计算机工程,2003,29(11)：23-25.
[104]陈振羽,周焰.关于计算机图像理解的知识与知识表达[J].红外与激光工程,2000,29(1)：73¨77.
[105]V N Vapnik.The Nature of Statistical Learning Theory[M], New York:Springer Verlag,2000.
[106]张文修,梁怡,徐萍.基于包含度的不确定推理[M],北京：清华大学出版社,2007.3.
[107]李德毅,杜鹢.不确定性人工智能[M],70-75,北京：国防工业出版社,2005.
[108]杨志民,刘广利.不确定性支持向量机原理及应用[M],32-38,北京：科学出版社,2007.4.
[109]苗夺谦,李道国.粗糙集理论、算法与应用[M],北京：清华大学出版社,2008.4.
[110]张文修,吴伟志,梁吉业.粗糙集理论与方法[M],北京：科学出版社,2003.
[111]张钦礼.基于支持向量机和模糊系统的机器学习方法及其应用研究[D].博士论文,江南大学,2009.
[112]Hsu Chih-Wei, Lin Chih-Jen. A Comparison of Methods for Multiclass Support Vector Machines[J]. Neural Networks, IEEE Transactions on,2002,13(2):415-425.
[113]L. Bottou, C. Cortes, J. S. Denker et al. Comparison of Classifier Methods:A Case Study in Handwritten Digit Recognition[C].1994,77-82 vol.2.
[114]刘江华,程君实,陈佳品.支持向量机训练算法综述[J].信息与控制,2002,2(1)：45-50.
[115]邓九英,杜启亮,毛宗源et a1.基于粗糙集与支持向量机的分类算法[J]华南理工大学学报(自然科学版),2008(05)：123-127.
[116]S. Santini, A. Gupta, R. Jain. Emergent Semantics through Interaction in Image Databases[J]. Knowledge and Data Engineering, IEEE Transactions on,2001,13(3):337-351.
[117]任越美.基于模糊领域本体语义的图像检索关键技术的研究[D].硕士论文,模式识别与智能系统；,江苏大学,中国,2008.
[118]Dubois D, Prade H. Rough Fuzzy Sets and Fuzzy Rough Sets[J]. Journal of General Systems, 1990,17(3):191-209.
[119]D Dubois, H Prade.Possibility Theory[M]:PLenum Press,1988.
[120]Hai Zhuge. Retrieve Images by Understanding Semantic Links and Clustering Image Fragments[J]. Journal of Systems and Software,2004,73(Compendex):455-466.
[121]唐宏,方涛,et a1.基于弱语义嵌入的图像相似性度量[J].高技术通讯,2006,16(1)：27-31.
[122]陈久军.基于统计学习的图像语义挖掘研究[D].博士论文,计算机科学与计算学院,浙 江大学,2006.
[123]张毅.基于高层语义的图像检索研究[D].硕士论文,情报学,西安电子科技大学,2007.
[124]陈世亮,李战怀,袁柳.一种基于多层语义相似性度量的图像检索方法[J].西北工业大学学报,2008(05)：588-591.
[125]A Budanisky, G Hirst. Semantic Distance in Wordnet:An Experimental, Application-Oriented Evaluation of Five Measures.[C]. Workshop on WordNet and Other Lexical Resources, Second Meeting of the North American Chapter of the Association for Computational Linguistics, Pittsburgh,USA,2001.
[126]Leacock. Claudia, Chodorow Martin. Combining Local Context and Wordnet Similarity for Word Sense Identification.[M]. Wordnet:A Lexical Reference System and Its Application, C. Fellbaum, ed., p.265-283, Cambridge, MA:MIT Press,1998.
[127]Fabrice Souvannavong, Bernard Merialdo, Benoit Huet. Improved Video Content Indexing by Multiple Latent Semantic Analysis[C]. Proceedings of the 3rd International Conference on Image and Video Retrieval (CIVR 2004), Dublin, IRELAND,2004,483-490.
[128]Fabrice Souvannavong, Bernard Merialdo, Benoit Huet. Latent Semantic Indexing for Semantic Content Detection of Video Shots[C]. Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME 2004) Taipei, TAIWAN,2004,1783-1786.
[129]Laurence A. F. Park, Kotagiri Ramamohanarao. An Analysis of Latent Semantic Term Self-Correlation[J]. ACM Transactions on Information Systems,2009,27(2):1-35.
[2]朱蓉.基于语义信息的图像理解关键问题研究[J].计算机应用研究,2009(04)：1234-1240.
[3]R.Jain. Proc. Us Nsf Workshop Visual Information Management Systems[C]. Redwood,CA,USA,1992.
[4]Arnold W.M.Smeulders, Marcel Wbrring, Simones Santini et al. Content-Based Image Retrieval at the End of the Early Years[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12).
[5]章毓晋.中国图像工程：2004[J]．中国图象图形学报,2005(05)：5-28.
[6]章毓晋.中国图像工程：2005[J]．中国图象图形学报,2006(05)：7-29.
[7]章毓晋.中国图像工程：2006[J]．中国图象图形学报,2007(05)：6-28.
[8]章毓晋.中国图像工程：2007[J]．中国图象图形学报,2008(05)：6-33.
[9]章毓晋.中国图像工程：2008[J]．中国图象图形学报,2009(05)：63-91.
[10]M. Flickner, H. Sawhney, W. Niblack et al. Query by Image and Video Content:The Qbic System[J]. Computer,1995,28(9):23-32.
[11]Rui Y, Huang T S, Mehrotra S. Content-Based Image Retrieval with Relevance Feedback in Mars[J]. Image Processing,1997(2):26-29.
[12]Zhuang Y, Liu X, Pan Y. Webscope-Cbvr:A Customized Content-Based Search Engine for Video on Www[C]. Proceeding of IS&T and SPIE Image and Video Communication and Processing,2000.
[13]Alan F. Smeaton, Peter Wilkins, Marcel Worring et al. Content-Based Video Retrieval:Three Example Systems from Trecvid[J]. International Journal of Imaging Systems and Technology, 2008,18(2-3):195-201.
[14]Peter Wilkins, Alan F. Smeaton, Noel E. O'Connor et al. K-Space Interactive Search[C]. Proceedings of the 2008 ACM International Conference on Content-Based Image and Video Retrieval, Niagara Falls, Canada,2008,555-556.
[15]Ork de Rooij, Cees G.M. Snoek, Marcel Worring. Mediamill:Fast and Effective Video Search Using the Forkbrowser[C]. Proceedings of the 2008 international conference on Content-based image and video retrieval, Niagara Falls, Canada,2008,561-562.
[16]Shi-Yong Neo, Huanbo Luan, Yantao Zheng et al. Visiongo:Bridging Users and Multimedia Video Retrieval[C]. Proceedings of the 2008 international conference on Content-based image and video retrieval, Niagara Falls, Canada,2008,559-560.
[17]贺莉娜.视频语语义特征提取的研究[D]．硕士论文,北京交通大学,2008.
[18]Wandell, B. A. The Synthesis and Analysis of Color Image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(1):2-13.
[19]Swain, M. J., D.H Ballard. Color Indexing[J]. International Journal of Computer Vision,1991, 7(1):11-32.
[20]Stricken M., M. Orengo. Similarity of Color[J]. SPIE Storage and Retrieval for Image and Viedeo Databases Ⅲ,1995,2185:381-392.
[21]黄朝兵,余胜生,周敬利.基于多邻域统计直方图的彩色图像检索[J]．小型微型计算机系统,2005,26(6)：1061-1064.
[22]J. Huang, S. R. Kumar. Image Indexing Using Color Correlogram[C]. IEEE Conference of ter Vision Pattern Recognition (CVPR 97), San.Juan, Puerto Rico,1997,762-768.
[23]Zachary J M. An Information Theoretic Approach to Content Based Image Retrieval[D]. Phd.Thesis, Louisiana State University and Agricultural and Mechanical College,2000.
[24]Jun ding Sun, Ximin Zhang, Jiangtao Cui et al. Image Retrieval Based on Color Distribution Entropy[J]. Pattern Recognition Letters,2006,27:1122-1126.
[25]Fuh C. S., S. W.Cho. Hierarchical Color Image Region Segmentation for Content-Based Image Retrieval System[J]. IEEE Transactions On Image Processing,2000,9(1):156-163.
[26]史迎春.基于内容的视频检索语义提取若干问题研究[D].博士论文,控制科学气工程,南京理工大学,南京,2005.5.
[27]Tamura, H., S. Mori. Texture Features Corresponding to Visual Perception[J]. IEEE Transactions on System,Man and Cybernetics,1978,8(8):460-473.
[28]Ma, W.-Y, B. S. Manjunath. A Comparison of Wavelet Transform Features for Texture Image Annotation[C]. Proceedings of IEEE International Conference on Image Processing (ICIP 1995), Washington DC, USA,1995,256-259.
[29]Pirrone R., M. L. Cascia. Texture Classification for Content-Based Image Retrieval[C]. International Conference on Image Analysis and Processing (ICIAP), Palermo, Italy,2001, 398-403.
[30]Hu, M.-K. Visual Pattern Recognition by Moment Invariants[J]. IEEE Transactions Information Theory,1962,8(2):179-187.
[31]曾璞.面向语义提取的图像分类关键技术研究[D].博士论文,控制科学与工程,国防科学技术大学,合肥,2009.
[32]H.Moravec. Towards Automatic Visual Obstacle Avoidance[C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence,1977,584-590.
[33]Crowley J. A Representation for Visual Information[D]. Ph.D., Mellon University, Carnegie, 1981.
[34]Tony Lindeberg. Feature Detection with Automatic Scale Selection[J]. International Journal of Computer Vision,1998,30(2):79-116.
[35]David G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision,2004,60:91-110.
[36]Krystian Mikolajczyk, Cordelia Schmid. Scale & Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision,2004,60:63-86.
[37]K. Mikolajczyk, C. Schmid. An Affine Invariant Interest Point Detector[C]. Proceedings of the 7th European Conference on Computer Vision (ECCV 2002), Copenhagen, Denmark,2002, 128-142.
[38]T. Kadir, M. Brady. Saliency, Scale and Image Description [J]. International Journal of Computer Vision,2001,45(2):83-105.
[39]Tinne Tuytelaars, Luc Van Gool. Matching Widely Separated Views Based on Affine Invariant Regions[J]. International Journal of Computer Vision,2004,59:61-85.
[40]J. Matas, O. Chum, M. Urban et al. Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions[J]. Image Vision Comput,2004,22(10 SPEC. ISS.):761-767.
[41]蔡红苹,雷琳,陈涛et aI.一种通用的仿射不变特征区域提取方法[J].电子学报,2008,36(4)：672-278.
[42]K. Mikolajczyk, T. Tuytelaars, C. Schmid et al. A Comparison of Affine Region Detectors[J]. International Journal of Computer Vision,2005,65:43-72.
[43]W. T. Freeman, E. H. Adelson. The Design and Use of Steerable Filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(9):891-906.
[44]J.G. Daugman. Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters[J]. Journal of the Optical Society of America A,1985,2(7):1160-1169.
[45]F. Schaffalitzky, A. Zisserman. Multi-View Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"[J]. Computer Vision—ECCV 2002,2002:414-431.
[46]T. Leung, J. Malik. Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons[J]. International Journal of Computer Vision,2001,43(1):29-44.
[47]K. Mikolajczyk, C. Schmid. A Performance Evaluation of Local Descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[48]N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005(CVPR 2005),2005,886-893
[49]J. van de Weijer, T. Gevers, A. D. Bagdanov. Boosting Color Saliency in Image Feature Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1): 150-156.
[50]A. Bosch, A. Zisserman, X. Munoz. Representing Shape with a Spatial Pyramid Kernel[C]. ACM,2007,401-408.
[51]K.E.A. van de Sande, T. Gevers, C.G.M. Snoek. A Comparison of Color Features for Visual Concept Classification[J]. ACM,2008,141-150.
[52]M. Stark, B. Schiele. How Good Are Local Features for Classes of Geometric Objects[C]. IEEE,2007,1-8.
[53]K. Mikolajczyk, B. Leibe, B. Schiele. Local Features for Object Class Recognaition[J].2005.
[54]Eakins, J. P., M. E. Graham. Content Based Image Retrieval.[J]. A report to the JISC Technology Application Programme,1999.
[55]D. Hong, J. K. Wu. Refining Image Retrieval Based on Context Driven Methods[C]. IS&SPIE I1 th Symposium on Electronic Imaging, San Jose,CA, USA,1999,581-592.
[56]A Jaimes, S.F. Chan. Model-Based Classification of Visual Information for Content-Based Retrieval[C]. Proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases VII, San Jose, CA,1999,402-414.
[57]王忠锋,孙正兴，王箭.语义图像检索研究进展[J].计算机研究与发展，2002、39(05)：511.523.
[58]高永英,章毓晋．基于多级描述模型的渐进式图像内容理解[J]．电子学报,2001,29(10)：1376-1380.
[59]Yu Wang, ChunXiao Xing, Lizhu Zhou. Video Semantic Models:Survey and Evaluation[J]. International Journal of Computer Science and Network Security(IJCSNS),2006,8(2A): 10-20.
[60]Luo J, A. E. Savakis. A Bayesian Network-Based Framework for Semantic Image Understanding[J]. Pattern Recognition,2005,38(919-934).
[61]Aksoy S., K. Koperski. Learning Bayesian Classifiers for Scene Classification with a Visual Grammar[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(3):581-589.
[62]王上飞,际恩红.基于支持向量机的图像情感注释和检索算法的研究[J]．模式识别 和人工智能,2004,17(1)：27-33.
[63]Yu Tao Han, Xiao Jun Qi. A Complementary Svms-Based Image Annotation System[C]. Processing of IEEE International Conference on Image 2005(ICIP 2005), Genova, Italy, 2005,1185-1188.
[64]K. S. Goh, E. Y. Chang, B. Li. Using One-Class and Two-Class Svms for Multiclass Image Annotation[J]. IEEE Transactions on Knowledge and Data Engineering,2005,17(10): 1333-1346.
[65]H Feng, T S Chua. A Bootstrapping Approach to Annotating Large Image Collection[C]. Workshop on Multimedia Information Retrieval in ACM Multimedia, Berkeley,CA,USA 2003.
[66]C P Town, D Sinclair. Content-Based Image Retrieval Using Semantic Visual Categories[J]. Society for Manufacturing Engineers,Technical Report MV01-211,2001.
[67]Liu W. Y., Y. Sun. Mialbum-a System for Photo Management Using the Simi-Automatic Image Annotation Approach [C]. Proceedings of ACM Multimedia, Los Angeles.USA,2000, 479-480.
[68]朱兴全,张宏江.Ifind：一个结合语义和视觉特征的图像相关反馈检索系统[J].计算机学报,2002,25(7)：681-688.
[69]He Xiaofei, KING O. Learning a Semantic Space from User's Relevance Feedback for Image Retrieval[J]. IEEE Transactions on Circuits and Systems for Video Technology,2003,13(1): 39-48.
[70]Han Jun Wei, Ngan King N, Li MingJing et al. A Memory Learning Framework for Effective Image Retrieval[J]. IEEE Transactions on Image Processing,2005,14(4):511-524.
[71]Srihari R. K., Z. Zhang. Intelligent Indexing and Semantic Retrieval of Multimodal Documents[J]. Information Retrieval,2000,2(2):245-275.
[72]Shen H. T., B. C. Ooi. Giving Meanings to Www Images[C]. Proceedings of the 8th ACM International Conference on Multimedia (ACM MM), Los Angeles, California,2000,39-47.
[73]张华,张淼.基于外部信息源的 WWW 图像语义提取研究[J].计算机科学,2006，33(4)：211-214.
[74]C Fellbaum.Wordnet:An Electronic Lexical Database[M], Cambridge,MA:MIT Press,1998.
[75]Luca Lombardi, Alfredo Petrosino. Distributed Recursive Learning for Shape Recognition through Multiscale Trees[J]. Image and Vision Computing,2007,25:240-247.
[76]高永英,章毓晋.基于多级描述模型的渐进式图像内容理解[J].电子学报,2001，29(10)：1376-1380.
[77]T. C. Chou, S. C. Cheng. Design and Implementation of a Semantic Image Classification and Retrieval of Organizational Memory Information Systems Using Analytical Hierarchy Process[J]. Omega-International Journal of Management Science,2006,34(2):125-134.
[78]Shyi-Chyi Cheng, Tzu-Chuan Chou, Chao-Lung Yang et al. A Semantic Learning for Content-Based Image Retrieval Using Analytical Hierarchy Process[J]. Expert Systems with Applications,2005,28(3):495-505.
[79]万华林,Morshed U.Chowdhury基于支持向量机的图像语义分类[J].软件学报,2003,14(11)：1891-1899.
[80]Boutell M., J. Luo. Bayesian Fusion of Camera Metadata Cues in Semantic Scene Classification[C]. Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'04), NY, USA,2004,623-630.
[81]Borja Calvo, Pedro Larranaga, Jose A. Lozano. Learning Bayesian Classifiers from Positive and Unlabeled Examples[J]. Pattern Recognition Letters,2007,28(16):2375-2384.
[82]粱竞敏.集成学习svm在图像检索中的应用[J].计算机工程与应用，2009(18)：182-184+230.
[83]G. Sheikholeslami, W. Chang, Zhang Aidong. Semquery:Semantic Clustering and Querying on Heterogeneous Features for Visual Data[J]. IEEE Transactions on Knowledge and Data Engineering,2002,14(5):988-1002.
[84]J. Goldberger, S. Gordon, H. Greenspan. Unsupervised Image-Set Clustering Using an Information Theoretic Framework [J]. IEEE Transactions on Image Processing 2006,15(2): 449-458.
[85]K. Barnard, D. Forsyth. Learning the Semantics of Words and Pictures[C]. Proceedings of the 8th International Conference on Computer Vision, Vancouver, BC, United States,2001, 408-415.
[86]James Z Wang, Jia Li. Learning-Based Linguistic Indexing of Pictures with 2-D Mhmms[C]. Proceedings of the ACM International Multimedia Conference and Exhibition, Association for Computing Machinery,2002,436-445.
[87]David M Blei, Andrew Y Ng, Michael I Jordan. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research,2003,3(4-5):993-1022.
[88]J. Jeon, V. Lavrenko, R. Manmatha. Automatic Image Annotation and Retrieval Using Cross-Media Relevance Models[C]. Proceedings of the 23th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 2003), Toronto, Ont., Canada,2003,119-126.
[89]J Jeon, R Manmatha. Using Maximum Entropy for Automatic Image Annotation[C]. Proceeding of the 3rd International Conference on Image and Video Retrieval (CIVR 2004), Dublin, IRELAND,2004,24-32.
[90]Rong Jin, Joyce Y. Chai, Luo Si. Effective Automatic Image Annotation Via a Coherent Language Model and Active Learning[C]. Proceedings of the 12th ACM International Conference on Multimedia (ACMM 2004), New York, NY, United States,2004,892-899.
[91]P Duygulu, K Barnard, J de Freitas et al. Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary[M]. Computer Vision-Eccv 2002, Lecture Notes in Computer Science A. Heyden, G. Sparr, M. Nielsen et al., eds., pp.349-354: Springer Berlin/Heidelberg,2006.
[92]J. Z. Wang, Li Jia, G. Wiederhold. Simplcity:Semantics-Sensitive Integrated Matching for Picture Libraries[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2001, 23(9):947-963.
[93]D. Brahmi, D. Ziou. Improving Cbir Systems by Integrating Semantic Features[C]. Proceedings of the 1 st Canadian Conference on Computer and Robot Vision (CRV'04) London, Ontario, Canada,2004,233-240.
[94]Yang C., M. Dong, et al. Learning the Semantics in Image Retrieval-a Natural Language Processing Approach[C]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Patern Recgnition Workshops (CVPRW'04),2004,137-143.
[95]李清勇,施智平et aI．基于纹理语义特征的图像检索研究[J]．计算机学报,2006,29(1)：116-123.
[96]Ritendra Datta, Jia Li, James Z. Wang. Content-Based Image Retrieval-Approaches and Trends of the New Age[C]. Proceedings of the 7th International Workshop on Multimedia Information Retrieval,In Conjunction with ACM International conference on Multimedia Systems, Singapore,2005,253-262.
[97]Yin X., M. Li, et al. Semantic Image Clustering Using Relevance Feedback[C]. IEEE International Symposium on Circuits and Systems (ISCAS'03),2003,904-907.
[98]王崇骏,杨育彬,陈世福.基于高层语义的图像检索算法[J].软件学报,2004，15(10)：1461¨1469.
[99]Haas M, Rijsdam J, Lew M. Relevance Feedback:Perceptual Learning and Retrieval in Bio-Computing, Photos, and Video[C]. Processings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieva, New York,2004,151-156.
[100]万小军杨建武.基于语义拓扑网的反馈学习技术[J].计算机工程,2005,31(1)：6-8.
[101]胡莹,王昱,丁明跃.一种改进的svm相关反馈图像检索方法[J].计算机应用研究,2005,22(1)：251¨254.
[102]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.
[103]张会章,张利霞,郭雷.图像理解[J].计算机工程,2003,29(11)：23-25.
[104]陈振羽,周焰.关于计算机图像理解的知识与知识表达[J].红外与激光工程,2000,29(1)：73¨77.
[105]V N Vapnik.The Nature of Statistical Learning Theory[M], New York:Springer Verlag,2000.
[106]张文修,梁怡,徐萍.基于包含度的不确定推理[M],北京：清华大学出版社,2007.3.
[107]李德毅,杜鹢.不确定性人工智能[M],70-75,北京：国防工业出版社,2005.
[108]杨志民,刘广利.不确定性支持向量机原理及应用[M],32-38,北京：科学出版社,2007.4.
[109]苗夺谦,李道国.粗糙集理论、算法与应用[M],北京：清华大学出版社,2008.4.
[110]张文修,吴伟志,梁吉业.粗糙集理论与方法[M],北京：科学出版社,2003.
[111]张钦礼.基于支持向量机和模糊系统的机器学习方法及其应用研究[D].博士论文,江南大学,2009.
[112]Hsu Chih-Wei, Lin Chih-Jen. A Comparison of Methods for Multiclass Support Vector Machines[J]. Neural Networks, IEEE Transactions on,2002,13(2):415-425.
[113]L. Bottou, C. Cortes, J. S. Denker et al. Comparison of Classifier Methods:A Case Study in Handwritten Digit Recognition[C].1994,77-82 vol.2.
[114]刘江华,程君实,陈佳品.支持向量机训练算法综述[J].信息与控制,2002,2(1)：45-50.
[115]邓九英,杜启亮,毛宗源et a1.基于粗糙集与支持向量机的分类算法[J]华南理工大学学报(自然科学版),2008(05)：123-127.
[116]S. Santini, A. Gupta, R. Jain. Emergent Semantics through Interaction in Image Databases[J]. Knowledge and Data Engineering, IEEE Transactions on,2001,13(3):337-351.
[117]任越美.基于模糊领域本体语义的图像检索关键技术的研究[D].硕士论文,模式识别与智能系统；,江苏大学,中国,2008.
[118]Dubois D, Prade H. Rough Fuzzy Sets and Fuzzy Rough Sets[J]. Journal of General Systems, 1990,17(3):191-209.
[119]D Dubois, H Prade.Possibility Theory[M]:PLenum Press,1988.
[120]Hai Zhuge. Retrieve Images by Understanding Semantic Links and Clustering Image Fragments[J]. Journal of Systems and Software,2004,73(Compendex):455-466.
[121]唐宏,方涛,et a1.基于弱语义嵌入的图像相似性度量[J].高技术通讯,2006,16(1)：27-31.
[122]陈久军.基于统计学习的图像语义挖掘研究[D].博士论文,计算机科学与计算学院,浙 江大学,2006.
[123]张毅.基于高层语义的图像检索研究[D].硕士论文,情报学,西安电子科技大学,2007.
[124]陈世亮,李战怀,袁柳.一种基于多层语义相似性度量的图像检索方法[J].西北工业大学学报,2008(05)：588-591.
[125]A Budanisky, G Hirst. Semantic Distance in Wordnet:An Experimental, Application-Oriented Evaluation of Five Measures.[C]. Workshop on WordNet and Other Lexical Resources, Second Meeting of the North American Chapter of the Association for Computational Linguistics, Pittsburgh,USA,2001.
[126]Leacock. Claudia, Chodorow Martin. Combining Local Context and Wordnet Similarity for Word Sense Identification.[M]. Wordnet:A Lexical Reference System and Its Application, C. Fellbaum, ed., p.265-283, Cambridge, MA:MIT Press,1998.
[127]Fabrice Souvannavong, Bernard Merialdo, Benoit Huet. Improved Video Content Indexing by Multiple Latent Semantic Analysis[C]. Proceedings of the 3rd International Conference on Image and Video Retrieval (CIVR 2004), Dublin, IRELAND,2004,483-490.
[128]Fabrice Souvannavong, Bernard Merialdo, Benoit Huet. Latent Semantic Indexing for Semantic Content Detection of Video Shots[C]. Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME 2004) Taipei, TAIWAN,2004,1783-1786.
[129]Laurence A. F. Park, Kotagiri Ramamohanarao. An Analysis of Latent Semantic Term Self-Correlation[J]. ACM Transactions on Information Systems,2009,27(2):1-35.