互联网环境下图像检索若干问题研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
随着多媒体技术和互联网技术的高速发展,互联网信息,不仅包含简单的文本数据,还包括大量的音频、图像、视频等多媒体信息。如何对大量的图像信息进行有效的管理并从中高效地检索到需要的图像已成为急需解决的问题,为了实现快速而准确的图像检索,基于Web的图像检索技术应运而生,并成为图像分析领域的研究热点之一。
     目前已经有不少的搜索引擎在提供网络图像的检索服务,如Google、Baidu、Soso、Ditto、PicSearch、Ixquick、Mamma等,但是这些搜索引擎对图像检索都采用基于关键词或描述性文本的检索方式,从本质上来说是一种基于文本的图像检索技术。这类检索技术最终将转换为对数据库中文本的检索,其优点是文本数据库检索理论已非常成熟,但存在一个致命的问题是需要对图像进行文本标注,标注工作量巨大,并且还存在“语义鸿沟”的问题,即图像的低层内容特征不能有效地描述高层语义问题。
     通过对国内外研究现状的分析,在已有研究工作的基础上,本文围绕基于内容的Web图像检索中存在的若干关键问题,从常见视觉特征的图像检索技术、图像特征选择、图像降维方法和图像相似性等四个方面对基于Web的图像检索技术展开了研究。
     本文的主要研究工作和创新点可概括如下:
     ①研究并分析了图像检索技术中存在的问题,介绍了基于视觉特征的图像检索的常用技术,为后续Web图像检索技术的研究做了铺垫。
     ②针对传统遗传算法在求解图像特征选择优化问题可能存在“早熟”或局部收敛等不足,在自适应遗传算法研究的基础上,将并行计算的原理应用于遗传算法,并对变异算子、交叉算子进行改进,提出了基于随机概率算子的双种群自适应遗传算法,并将其成功用于图像特征选择优化,改善了图像特征选择的性能。
     ③提出了一种利用HSV颜色模型提取图像主色的图像降维算法。将RGB颜色模型转换为基于视觉感知的HSV空间,然后对图像进行HSV特征72维量化,同时按每维上的特征值从大到小进行排序,按照一个阈值,取前d个维数作为图像的本征维数,从而达到快速降维的目的。研究表明,这种降维方式无需事先指定需要降维的维数,能较好地利用图像本征维数,达到降维的目的。
     ④提出了一种基于区域Shannon互信息的图像相似性度量方法。在Shannon熵、联合熵、条件熵的基础上,将图像之间的Shannon互信息作为相似性度量函数进行图像相似性度量,针对Shannon互信息需要进行三次归一化处理、计算量偏大的问题,引入图像的空间位置,对Shannon互信息的计算进行了改进,减小了图像互信息的计算工作量,提高了图像检索的性能。
     ⑤对面向Web的图像检索系统模型框架进行深入研究,初步搭建了基于图像内容的图像检索系统平台,将检索系统分为界面设计、网络蜘蛛、图像预处理、图像特征提取、图像特征选择、相似性度量和相关反馈等部分。在现有文本搜索引擎的基础上,添加图像搜索功能,建立72叉树作为图像索引和分类,实现了Web页面图像的抓取、数据库存储和相似性度量的过程,真正意义上实现了基于目标图像的Web图像检索。
With the rapid development of the multimedia technology and internet technology, information on the internet contains not only simple text data, but also large amounts of audio, image, video and other kinds of multimedia information. How to effectively manage a large number of image information, and that from which the images can be retrieved efficiently has become an urgent problem. In order to achieve fast and accurate image retrieval, Web-based image retrieval techniques have been developed and become one of the hot areas of research on the image analysis.
     There are already many search engines to provide image search services such as Google, Baidu, Soso, Ditto, PicSearch, Ixquick, Mamma, etc. but these search engines are all based on keywords or descriptions texts, so they are essentially the text-based image retrieval. This type of search technology will eventually be converted into a kind of the database search aiming at the text, featuring a retrieval theory being already very mature. Nonetheless, there may be a fatal problem, that is, text labels are required on the image, and the workload is huge. Apart from that, the "semantic gap" is another problem, that is, low-level content feature of an image is unable to provide an effective description of the high-level semantics.
     Based on the analysis of the research survey of home and abroad, combined with the existing research work, this paper mainly provided the research results on the Web-based image retrieval technique with regard to its several key issues, where four aspects are discussed on common visual features, including search technology, feature selection, dimensionality reduction methods, and similarity measure. Major research work and innovations of this paper are as follows:
     (1) Research and analysis are given to the issues that may be found in image retrieval techniques, followed by introducting the technology of image retrieval based on visual feature, paving the way for the follow-up study on Web image retrieval.
     (2) Targeting at the traditional genetic algorithm for solving the image feature selection optimization with the problems that may be premature or lack in local convergence, the adaptive genetic algorithm is combined with the principle of parallel computing applied in genetic algorithms. Using the improved mutation operator and crossover, a genetic algorithm based on two-population adaptive random probability operator is proposed and successfully used for image feature selection and optimization, achieving better performance.
     (3) A dimension reduction method based on the HSV color model to extract dominant color is proposed. RGB color model is converted to the HSV space based on visual perception. Then, the image is undergone with 72-dimensional vector of HSV features. This is followed by sorting in a descending order by the feature value of each dimension. According to the indicated threshold, the first d dimensions are taken as the number of the intrinsic dimensions of an image, so as to achieve the purpose of quick dimensionality reduction. Studies have shown that this dimensionality reduction method can be applied without specifying the dimension number of the required reduction, and it can take advantage of the intrinsic dimensions of an image to achieve the purpose of dimensionality reduction.
     (4) An image measurement method is proposed based on the area similarity of mutual information. On the basis of the Shannon entropy, joint entropy, conditional entropy, Shannon mutual information between images is taken as a similarity functions. For the issue on Shannon mutual information that requires three normalizations involving a large amount of computation, Shannon mutual information calculation is optimized to reduce the computational workload by the image space, thus improving the image retrieval performance.
     (5) Research is performed on the model framework for Web-oriented image retrieval system, initially building up an image retrieval system based on image content . Specifically, the system model is divided into interface design, web spider, image pre-processing, image feature extraction and selection, similarity measure and the feedback. Image search function is added up to the text search engine, creating 72-tree for image index and classification, achieving really Web image retrieval based on target image, by achieving a Web page image capture, database storage and similarity measure.
引文
[1] R.Datta,D.Joshi,J.Li,and J.Z.Wang.Image Retrieval:Ideas,Influences,and Trends of the New Age[J].ACM Computing Surveys,2008,40(2):1-60.
    [2] King,C.H.Ng,and K.C.Sia.Distributed Content-Based Visual Information Retrieval System on Peer-To-Peer Networks [J].ACM TOIS,2004,22:477-501.
    [3] H.Q.Nguyen,and Q.T.Ngo.A Novel Method for Content Based Image Retrieval Using Color Features [J].IJCSES International Journal of Computer Sciences and Engineering Systems,2009,3(1):281-288.
    [4] H.B.Kekre and S.D.Thepade.Image Retrieval using Augmented Block Truncation Coding Techniques[C].International Conference on Advances in Computing,Communication and Control (ICAC3’09),Mumbai, India,2009: 384-390.
    [5] Alaa M.Riad,Ahmed Atwan,Hazem M.El-Bakry et al.An Intelligent Distributed Algorithm for Efficient Web Image Retrieval[J].International Journal Of Communications,2009,3(3): 63-76.
    [6] M.Flickner et al..Query by image and video content:the QBIC system[J]. IEEE Computer,1995,28(9): 23–32.
    [7]李勇.基于内容的图像检索技术研究[D],吉林大学,2009.
    [8] A.Pentland,R.W.Picard,S. Sclaroff. Photobook:content-based manipulation of image databases [J].International Journal of Computer Vision,1996,18(3):233-254.
    [9] S.Mehrotra,Y.Rui,M.Ortega et al.Supporting content based queries over images in MARS[C] , Proceedings of the 1997 IEEE International Conference on Multimedia Computing and Systems,ICMCS,Ottawa,Ont,Can,1997:632–633.
    [10] C.Nastar,M.Mitschke,C.Meilhac at al.Surfimage:a flexible content-based image retrieval system[C] The 6th ACM Int.Multimedia Conf.(MM’98),Bristol,England, 1998:339–344.
    [11] Bach J R, Fuller C, Gupta A, et al. The Virage Image Search Engine: An Open Framework for Image Management[C]. In Proc. SPIE, Storage and Retrieval for Still Image and Video Databases IV, vol 2670. San Jose, CA, USA, 1996:76-87.
    [12] Q.Iqbal,K.Aggarwal.Feature integration,multi-image queries and relevance feedback in image retrieval[C],6th International Conference on Visual Information Systems (VISUAL 2003),Miami,Florida , 2003,467-474.
    [13] Alberto Amato,Vincenzo Di Lecce.A knowledge based approach for a fast image retrieval system[J].Image and Vision Computing,2008,26( 11):1466-1480.
    [14]古毅.基于内容的图像检索中索引技术的研究及系统实现[D],重庆大学,2006.
    [15]白雪生,廖春元,徐光佑,史元春.ImgRetr—一个基于内容的图象检索系统[C].第七届全国多媒体技术学术会议,长沙,1998,10:289-294.
    [16]薛向阳,罗航哉.一种新的颜色相似度定义及其计算方法[J] .计算机学报,1999,22(9):918-922.
    [17]梅承力,杨景涛,周源华.基于空间数据结构的图像检索[J] .上海交通大学学报,2002,36(6):796-798,811.
    [18]周向东,张亮,张琪等.基于反馈日志分析的图像检索相关反馈方法[J] .计算机研究与发展,2004,41(1):111-117.
    [19]张亮,施伯乐,周向东等.发掘相关反馈日志中关联信息的图像检索方法[J].软件学报, 2004,15(1):41-48.
    [20]张亮,周向东,张琪等.图像检索中基于长期学习的动态用户模型[J].软件学报,2005,16(2):233-238.
    [21]袁进,周向东,王梅等.改进的基于支持向量机的Web图像检索相关反馈方法[J].计算机研究与发展,2007,z3:166-170.
    [22]许红涛,周向东,向宇等.一种自适应的Web图像语义自动标注方法[J].软件学报,2010,9:2183-2195.
    [23] Wang Xuchu, Li Jianwei, Niu Yanmin, et al.Extraction of stable points from fingerprint images using zone Could-be-in theorem[C].International Conference on Biometrics, ICB 2006,Hong Kong, China,251-257.
    [24]洪沙,张建勋.基于图像颜色特征检索技术的材料腐蚀监测系统[J] .重庆大学学报:自然科学版,2006,29(10):47-50.
    [25]尚赵伟,唐远炎,刘正岐等.基于多小波统计特征的纹理图像检索[J].重庆大学学报:自然科学版,2007,30(9):64-70.
    [26]尚赵伟,唐远炎,房斌等.基于相对熵和复小波变换的纹理图像检索[J].重庆大学学报:自然科学版,2008,31(5):541-548.
    [27]姜琳,房斌,唐远炎等.小波域多方向信息融合的纹理图像检索[J].计算机工程,2010,36(5):215-217.
    [28]邱开金,肖国强,张为群.基于块边缘特征直方图的图像检索[J].计算机科学,2006,33(4):215-217.
    [29]邱开金,肖国强,江健民.基于游程编码的块边缘模式图像检索算法[J].计算机应用,2006,26(9):2074-2076,2080.
    [30]王剑峰,肖国强,江健民.基于HSI色彩空间累加直方图的图像检索算法[J].计算机工程与科学,2007,29(4):55-58.
    [31]黄超,肖国强,江健民.基于显著闭合边界的压缩域图像检索[J].计算机工程与设计,2008,29(9):2286-2289,2292.
    [32]陈蔚,肖国强.基于多特征的图像检索算法[J].计算机工程与设计,2008,29(17):4507-4510.
    [33]秦小铁,邱玉辉,尹世群.一种基于自学习的动态语义网络的图像检索方法[J].西南师范大学学报:自然科学版,2010,35(2):190-193.
    [34]钟洪.基于本体的图像检索[D].中南大学, 2008
    [35]向前.基于本体的图像检索系统[D].北京交通大学, 2008.
    [36]孙亮.基于本体的图像语义检索技术研究[D] .西北师范大学,2009.
    [37]荚济民.基于互联网数据集的图像标注技术研究[D].中国科学技术大学,2009.
    [38] Changbo Yang , MingDong , and JingHua . Region-based image annotation using asymmenetrical support vector machine-based multiple-instance learning[C].In CVPR’06:Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC,USA,IEEE Computer Soeiety,2006 :2057-2063.
    [39] Claudio Cusano,Gianluigi Ciocea,and Raimondo Sehettini.Image annotation using svm[C].Proceedings of SPIE - The International Society for Optical Engineering,San Jose, CA, United states ,2004,5304:330-338.
    [40] Yuli Gao,JianPing Fan,Xiangyang Xue,and Ramesh Jain.Automatic image annotation by incorporating feature hierarehy and boosting to scale up svm classifiers[C] . In MULTIMEDIA’06:Proceedings of the 14th annual ACM international conference on Multimedia,New York,NY,USA,2006 :901-910.
    [41] Jia Li and J.Z.Wang.Automatic linguistic indexing of pictures by a statistical modeling aproach[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(9):1075-1088.
    [42] E.Chang,Kingshy Goh,G.Sychay,and GangWu Cbsa:content-based soft annotation for multimodal image retrieval using bayes point machines[J].Circuits and Systems for Video Technology,IEEE Transactionson,2003,13(l):26-38.
    [43] Gustavo Carmeiro,Antoni B.Chan,and Pedro J.Moreno.Supervised learning of semantic classes for image annotation and retrieval[J].IEEE Trans.Pattern Anal.Mach.Intell,29(3):394-410,2007.
    [44] Yohan Jin,Latifur Khan,Lei Wang,and Mamoun Awad.Image annotations by combining multiple evidence & wordnet[C].In MULTIMEDIA '05 :Proceedings of the 13th annual ACM international conference on Multimedia ,New York,NY,USA,2005:706-715.
    [45] Changhu Wang,Feng Jing,Lei Zhang,and HongJiang Zhang.Image annotation refinementusing random walk with restarts[J].In MULTIMEDIA '06 :Proceedings of the 14th annual ACM international conference on Multimedia ,New York,NY,USA,2006:647-650.
    [46]李志欣,施智平,刘曦,史忠植.建模连续视觉特征的图像语义标注方法[J].计算机辅助设计与图形学学报,2010,8:1412-1420.
    [47]李国臣,王瑞波,李济洪.基于条件随机场模型的汉语功能块自动标注[J].计算机研究与发展,2010,2:336-343.
    [48] Arnold W.M.Smeulders,Senior Member,Marcel Worring,Simone Santini,Amarnath Gupta,Ramesh Jain.Content-based image retrieval at the end of the early years[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12): 1349-1380.
    [49] Ritendra Datta ,Jia Li,James Z.Wang.Content-based image retrieval:approaches and trends of the new age[C].in Proceeding MIR '05 :Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval,2005:253—262
    [50] Cai D.,He X.,Ma W.Y,etal.Organizing WWW images based on the analysis of page layout and web link structure [C].2004 IEEE International Conference on Multimedia and Expo (ICME) ,Taipei,Taiwan ,2004 ,l:113-116.
    [51] Wang X.J.,Ma W.Y.,Xue G.R.et al.Multi-model similarity propagation and its application for web image retrieval[C].Proceedings of the 12th annual ACM international conference on Multimedia,2004:944-951.
    [52] Woodruff A.,Fauiring A.,Rosenholtz R.,et al.Using thumbnails to search the Web[J]:ACM Press New York,NY,USA,2001:198-205.
    [53] Xue G.R.,Zeng H.J.,Chen Z.,Ma W.Y.,Yu Y..Similarity spreading:a unified framework for similarity caleulation of interrelated objects[C],Proceedings of the 13th international World Wide Web conference on Alternate track papers &posters,New York,NY,USA,2004,460-461.
    [54] Silva I.,Ribeiro-Neto B.,Calado P.,et al.Link-based and content-based evidential information in a belief network model[J]:SIGIR Forum (ACM Special Interest Group on Information Retrieval),2000:96-103.
    [55] B.Bradshaw.Semantic based image retrival:a probabilistic approach[C].ACM International Conference on Mulimedia.Los Angeles,California,USA, 2000,4:167-176.
    [56] J . M . Corridoni , A . Del Bimbo , and P . Pala . Image retrieval by color semantics[J].Multimedia Systems,1999,7(3):175-183.
    [57] R.Zhao and W.I.Grosky.Negotiating the semantic gap:from feature maps to semantic landscapes[J].Pattern Recognition,2002,35(3):593-600.
    [58] A.Mojsilovic,J.Kovacevic,J.Hu,R.J.Safranek,and S.K.GanaPathy.Matchingand retrieval based on the vocabulary and grammar of color Patterns[J].IEEE Transactions on Image Processing,2000,9(1):38-54.
    [59] Y.Lu,H.Zhang,W.Liu,and C.Hu.Joint semantics and feature based image retrieval using relevance feedback[J].IEEE Trans.on Multimedia,2003,5(3):339-327.
    [60] F.Jing,M.Li,H.J.Zhang,and B.Zhang.Relevance feedback in region-based image retrieval[J].IEEE Trans.on Circuits and Systems for Video Technology,2004, 14(5): 672-681.
    [61]黄启宏,刘钊.流形学习中非线性维数约简方法概述[J].计算机应用研究,2007,24(11):19-25.
    [62] John shawe-Tayloy, Nello Cristianini.模式分析的核方法(英文版)[M].北京:机械工业出版社,2005,1.
    [63]胡永刚,吴翊,王洪志,卜江.高维数据降维的DCT变换[J].计算机工程与应用,2006,32:21-23,30.
    [64]赵连伟,罗四维,赵艳敞,刘蕴辉.高维数据流形的低维嵌入及嵌入维数研究[J].软件学报,2005,16(8):1423-1430.
    [65] Qinggang Wang , Jianwei Li.Combining local and global information for nonlinear dimensionality reduction[J].Neurocomputing,2009,72:2235-2241.
    [66]王庆刚,李见为.最大方差展开的快速松弛算法[J].计算机研究与发展,2009,6:988-994.
    [67]王长虎.互联网环境下大规模图像的内容分祈、检索和自动标注的研究[D].中国科学技术大学,2009.
    [68] D.Androutsos,K.N.Plataniotiss,and A.N.Venetsanopoulos.Distance measures for color image retrieval[C].IEEE International Conference on Image Processing ,Chicago,IL,USA ,1998,2:770-774.
    [69] Zhixiang Chen and Binhai Zhu.Some formal analysis of rocchio's similarity-based relevance feedback algorithm[J].Inf.Retr.,5(l):61-86,2002.
    [70] Rouhollah Rahmani , Sally A . Goldman , Hui Zhang , John Krettek , and Jason E.Fritts.Localized content based image retrieval[C].InMIR'05:Poceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval,New York,NY,USA,2005,227-236.
    [71] Oded Maron and Aparna Lakshmi Ratan.Multiple-instance learning for natural scene classification[C].In The Fifteenth International Conference on Machine Learning,Morgan Kaufmann,1998 , 341-349.
    [72] Rouhollah Rahmani and Sally A.Goldman.Missl:Multiple-instance semi-supervised learning[C].In In Proceedings of the International Conference on Machine Learning(ICML),2006,705-712.
    [73] Changhu Wang,Lei Zhang,and Hong-Jiang Zhang.Graph-based multiple-instance learning for object-based image retrieval[C].In MIR'08:Proceeding of the 1st ACM international conference on Multimedia information retrieval,NewYork,NY,USA, 2008:156-163.
    [74]李云,刘嘉敏等.图像检索中相关反馈技术的特性研究[J].计算机工程.2004,30(7):128-129.
    [75] Lee C,Ma W Y,Zhang H J.Information Embedding Based on Retrieval Users Relevance Feedback for Image[C].Proceedings of SPIE - The International Society for Optical Engineering.Boston,MA,USA,1998,3846: 294-304.
    [76]张磊,林福宗,张钹.基于支持向量机的相关图像检索算法[J].清华大学学报(自然科学版).2002,(1):80-83.
    [77]姚敏.数字图像处理[M].北京:机械工业出版社,2008,3.
    [78]周明全,耿国华,韦娜.基于内容图像检索技术[M].北京:清华大学出版社,2007,7.
    [79]魏宝刚,李向阳,鲁东明等.彩色图像分割研究进展[J].计算机科学,1999,26(4):59-62.
    [80]徐旭,朱森良,梁倩卉等.一种用于CBIR系统的主色提取及表示方法[J] .计算机辅助设计与图形学学报,1999,11(5):385-388.
    [81]罗时光.基于颜色的图像检索系统的研究[D].长春理工大学,2010.3.
    [82]胡浩.基于内容的图像检索技术研究与嵌入式系统实现[D] .江南大学,2008.
    [83]章毓晋.图像工程(上)图像处理和分析[M].北京:清华大学出版社,1999,3.
    [84]章毓晋.图像工程(上册)图像处理(第二版)[M].北京:清华大学出版社,2006,3.
    [85] Gonzalez.R.C,Woods.R.E.Digital Image Processing[M],3nd ed.Addision-Wesley,1992.
    [86] Palus.H.Colour Space.In:Sangwine,Stephen J.; Horne,Robin E.N.(Eds.)[M].The Colour Image Processing handbook.Chapman & Hall,1998,456.
    [87] Shih,T.K.,Huang,J.-Y.,Wang,C.S at al.An intelligent content-based image retvieval system based oncolor,shape and spatial relations[J].Proc.Natl.Sci.Counc.ROC(A),2001,25(4):232-243.
    [88] Lei Liang,Wang Xue,Yang Bo,et al.Image Dimensionality Reduction based on the HSV Feature[C].Proceedings of the 9th IEEE International Conference on Cognitive Informatics,ICCI 2010,Beijing, China,2010:127-131.
    [89] Mehtre B.M.,Kankanhalli M.S.,Narasimhalu A.D.,et al.Color matching for image retrieval[J].Pattern Recognition Letters,1995,16(3):325-331.
    [90] Swain M.J.,Ballard D.H.Color indexing[J].International Journal of Computer Vision,1991,7(1):11-32.
    [91] Hafner J,Sawhney H.S.,Equitz W ,et al.Efficient color histogram indexing for quadratic form distance functions[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(7):729-736.
    [92] Stricker M,Orengo M.Similarity of color images[C].Proceedings on Storage and Retrieval for Image and Video Databases III,1995,2420:381-392.
    [93] B.V.Funt and G.D.Finlayson.Color Constant Color Indexing[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1995,17(5):522-529.
    [94] Wan X.,Kuo C.C.J.New approach to image retrieval with hierarchical color clustering[J].IEEE Transactions on Circuits and Systems for Video Technology,1998, 8(5):628-643.
    [95]任雪梅.图像颜色特征检索算法的研究及DSP实现[D].吉林大学,2007.
    [96] Zhang Dengsheng , Lu Guojun . Evaluation of similarity measurement for image retrieval[C].Proceedings of 2003 International Conference on Neural Networks and Signal Processing,ICNNSP'03,Nanjing, China ,2003,2:928-931.
    [97]黄祥林,沈兰荪.基于内容的图像检索技术研究[J].电子学报,2002,30(7):1065一1071.
    [98]孙国霞.基于无监督成分分析的图像检索方法研究[D].山东大学,2007.
    [99]韦素云,吉根林.基于加权颜色直方图和颜色对的图像检索系统[J].南京师范大学学报(工程技术版),2005,5(1):53-56.
    [100]陈传波,金先级.数字图像处理[M].北京:机械工业出版社,2004.7.
    [101]李立红.基于内容的图像浏览和检索及实验系统的开发[D].西安科技大学,2005.
    [102] Niblack W.Barber R.Equitz W.,et al.QBIC project: querying images by content, using color, texture, and shape[C].Storage and Retrieval for Image and Video Databases,San Jose, CA, USA ,1993,1908:173-187.
    [103] Rui,Y.,Huang,T.S.,Ortega,M.and Mehrotra.Relevance feedback:A power tool for interactive content-based image retrieval[J].IEEE Transactions on Circuits and Systems for Video Technology,1998,8(5):644-655.
    [104]茹立云,彭潇,苏中等.基于内容图像检索中的特征性能评价[J].计算机研究与发展.2003,4011:1566-1570.
    [105]樊亚春,耿国华,周明全.用不变矩和边界方向进行形状检索[J].小型微型计算机系统,2004,25(4):652-659.
    [106] TAN Kian2Lee,OoiBeng Chin,Yee Chia Yeow.An evaluation of color2spatial retrieval technique for large image database[ J ].Multimedia Tools and Applications,2002,14 (1) :255-278.
    [107]高燕,胡学龙,阮文佳等.基于综合特征的彩色图像检索算法[J].江南大学学报:自然科学版,2009,8(6):670-673.
    [108] M.Dash,H.Liu.Feature Selection for Classification[J].Intelligent Data Analysi,1997(1):131-156.
    [109] Jain A,Zongker D.Feature Selection:Evaluation,Application,and Small Sample Performance[J].IEEE transactions on pattern analysis and machine intelligence ,1997,19(2):153-158.
    [110] Xing E.P.,jordan M.I,karp R.M.Feature selection for high-dimensional genomic microarray data[C].In Proceedings of the Eighteenth International Conference on Machine Learning ,2001:601-608.
    [111]边肇祺,张学工.模式识别第2版[M].北京:清华大学出版社,2000.
    [112] H Almuallim , T G Dietterich . Efficient Algorithms for Identifying Relevant Features[C].Proc.9th Canadian Conf.on AI,1992:38-45.
    [113]安磊.一种基于遗传算法的数据挖掘技术的研究与应用[D].河海大学,2001.
    [114]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999,6.
    [115]王银年.遗传算法的研究与应用——基于3PM交叉算子的退火遗传算法及应用研究[D],江南大学,2009:4-13.
    [116]王四春.GP算法中适应度函数的光滑拟合与调整参数方法研究[J].自动化学报.2006,(3):23—30.
    [117]任庆生,叶中行,曾进等.对常用选择算子的分析[J].上海交通大学学报,2000,34(4):564-566.春.GP算法中适应度函数的光滑拟合与调整参数方法研究[J].自动化学报.2006,(3):23—30.
    [118]王小平,曹立明.遗传算法[Ml.西安交通大学出版社.2002.
    [119] Goldberg D E.Genetic Adaptation in Computer Control Systems[D].Miehigan:University of Miehigan,1971.
    [120]杨秋辉,游志胜,冯子亮等.一种改进的基于遗传算法的多跑道到达飞机调度[J].四川大学学报(工程科学版),2006, 38(2):141-145.
    [121] Liu Jenn-Long, Chen Chia-Mei.Improved intelligent genetic algorithm applied to long-endurance airfoil optimization design[J].Engineering Optimization,2009,41(2):137-154.
    [122] Dong Lili,Xue Chaogai, Li Guohua.An improved immune genetic algorithm based on niche algorithm and its application[C].2010 2nd International Symposium on Information Engineering and Electronic Commerce, IEEC 2010,2010:17-20.
    [123] Wang Zhenchao,Duan Haibin1, Zhang Xiangyin.An improved greedy genetic algorithm for solving travelling salesman problem[C].5th International Conference on Natural Computation,ICNC 2009,2009,5:374-378.
    [124] An Jian-Cheng,Jin Hai-Juan, Liu Chaohui.Improved real-coding genetic algorithm[C]. Proceedings - International Conference on Networks Security, Wireless Communications and Trusted Computing, NSWCTC 2009,2009,2:696-698.
    [125] Ming Huang,Nan Liu, Xu Liang.An improved niche genetic algorithm[C].Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009,2009,2:291-293.
    [126] Liu Gang,Wang Xuemei,Yang Lina.An improved genetic algorithm and its application[C].Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010,2010,7:3788-3791.
    [127] Min Gu,Feng Yang.An improved genetic algorithm based on polygymy[C].3rd International Symposium on Intelligent Information Technology and Security Informatics, IITSI 2010,2010:371-373.
    [128]侯世英,索利娟,郑含博.改进遗传算法在混合有源滤波器的研究[J].电力系统保护与控制,2010,1:70-74.
    [129]雷亮,汪同庆,彭军,杨波.改进的自适应遗传算法应用研究[J].计算机科学,2009,36(6):203-205,247.
    [130]雷亮,李善君,彭军.改进的遗传算法在Web使用挖掘中的应用[J].计算机工程与应用, 2009, 45(8): 135-137,171.
    [131] (加)Jiawei Han;Micheline Kamber著.范明/孟小峰译.数据挖掘概念与技术(原书第2版)[M].北京:机械工业出版社,2007:151-154.
    [132]曾凡超,朱征宇,邓欣,何兴无.车辆路径问题的改进的双种群遗传算法[J].计算机工程与设计, 2007,28(20):4999.
    [133]陈显毅.图像配准技术及其MATLAB编程实现[M].北京:电子工业出版社,2009:143-149.
    [134]雷亮,汪同庆,杨波.图像关联规则挖掘研究[J].计算机应用研究,2009,26(6):2374-2376.
    [135]雷亮,汪同庆,杨波.改进遗传算法在图像挖掘中的应用[J].计算机工程与应用,2009,45(3):38-41.
    [136] Liang LEI,TongQing WANG,Bo YANG,Xue WANG.Image Retrieval Based on Shannon Entropy and the AGAR[J].Journal of Computational Information Systems,2009,5(6):1847- 1853.
    [137] Liang Lei,Tongqing Wang,Xue Wang,Bo Yang.The Research of Association Rules Based on Image Mining[J].Journal of Information & Computational Science,2010,7(2):391-397.
    [138]王小平.遗传算法—理论、应用及软件实现[M].西安:西安交通大学出版社,2002.
    [139]赵丽娜,刘培玉,朱振方.自适应遗传算法在特征选择中的改进及应用[J].计算机工程与应用,2009,45(7):39-41,64.
    [140]黄炜,黄志华.一种基于遗传算法和SVM的特征选择[J].计算机技术与发展,2010,20(6):21-24.
    [141]陈卫东,刘素华.基于遗传算法的图像特征选择[J].计算机工程与应用,2007,43(28):78-80.
    [142]冯莉,李满春,李飞雪.基于遗传算法的遥感图像纹理特征选择[J].南京大学学报(自然科学),2008,44(3):310-318.
    [143]吴进文,赵晓翠,陈苗苗.基于遗传算法的高维特征选择的研究[J].郑州轻工业学院学报(自然科学版),2010,25(2):75-78.
    [144]赵云,刘惟一.基于遗传算法的特征选择方法[J].计算机工程与应用,2004,40(15):52-54.
    [145]冯海亮.流形学习算法在人脸识别中的应用研究[D].重庆大学,2008.
    [146] Joshua B.Tenenbaum,Vin de Silva and John C.Langford.A Global Geometric Framework for Nonlinear Dimensionality Reduction[J].Science,2000,290(5500):2319-2323.
    [147]周红,吴炜,滕奇志等.流形学习中的算法研究[J].计算机应用研究,2007,24(7):214-217.
    [148]黄启宏.流形学习方法理论研究及图像中应用[D].电子科技大学, 2007.
    [149]魏艳涛.基于流形学习的数据降维方法研究[D].华中科技大学,2008,4.
    [150] Donoho DL,Grimes C.Hessian eigenmaps:New locally linear embedding techniques for high-dimensional data[C].Proceedings of the National Academy of Sciences of the United States of America,2003,100(10):5591?5596.
    [151] Zhang CS,Wang J,Zhao NY,Zhang D.Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction[J].Pattern Recognition,2004,37(1):325?336.
    [152] Polito M , Perona P . Grouping and dimensionality reduction by locally linear embedding[J].Neural Inform Process Systems,2001,1255?1262.
    [153] Mikhail Belkin ,Partha Niyogi.Laplacian Eigenmaps and Speetral Technliques for Embedding and Clustering[J].Advances in Neural Information Processing System,2002,14:585-591.
    [154]李杰.基于内容的图像检索方法研究[D].中国科学技术大学,2007.
    [155] He X F,Niyogi P.Locality Preserving Projections[J].Advances in Neural Information Processing Systems Vancouver Canada,2003:253-268.
    [156]王庆刚,李见为.具有局部结构保留性质的PCA改进算法[J].模式识别与人工智能,2009,3:388-392.
    [157] Ke Lu,et al.Image Retrieval Using Dimensionality Reduetion[J].Lecture Notes Computer Scienee,2004,3314:775-781.
    [158]鲁珂.流形学习方法在Web图像检索中的应用研究[D].电子科技大学,2006.
    [159]曹莉华,柳伟,李国辉.基于多种主色调的图像检索算法研究与实现[J].计算机研究与发展,1999,36(l):96-100.
    [160]徐旭,朱淼良,梁倩卉等.一种用于CBIR系统的主色提取及表示方法[J].计算机辅助设计与图形学,1999,11 (5) :385-388.
    [161]袁昕,吴春明,朱淼良等.基于主色选择的CBIR检索[J] .计算机研究与发展,2002,39(9): 1120-1126.
    [162]刘为.基于内容图像检索关键技术的研究[D].吉林大学,2010,6.
    [163]刘建.高维数据的本征维数估计方法研究[D].国防科学技术大学,2005.
    [164] J,Bruske and G.Sommer.Intrinsic Dimensionality Estimation with Optimally Topology Preserving Maps[J].IEEE Transactions on pattern Analysis and Machine Intelligence,1998,20(5):572-575
    [165] I.T.Jolliffe.Principal Component Analysis[M].Springer-Verlag,New Tork,1989.
    [166] M.Kirby.Geomatic Data Analysis:An expirical Approach to Dimensionality Reduction and Study of Patterns[M].New York,NY:John Wiley and Sons,2001.
    [167] D.H.Schwartzmann,J.J.Vidal.An Algorithm for Determingning the Topological Dimensionality of Point clusters[J].IEEE Transactions on Computers,1975,24(12):1175-1182.
    [168] R.S.Bennet.The Intrinsic Dimensionality of Signal Collection[J].IEEE Transactions on Information Theory,1969:517-525.
    [169] K.Fukunaga ang D,R,Olsen.An Algorothm for Finding Intrinsic Dimanesionlity of Data.IEEE Transactions on Computer,1971,C-20(2):176-183.
    [170] C.K.Chen and H.C.Andrews.Nonlinear Intrinsic Dimensinality Computations[J].IEEE Transactions on Computer ,1974,23(2):178-184.
    [171] K.Pettis,T.Bailey,A.K.Jain,R.Dubes.An Tintrinsic Dimensionality Estimator from Near neighbor Information[J] . IEEE Transactions on Patter Analysis and Machine Intelligence,1979:25-27.
    [172] E.Levina,P.Bickel.Maximum Likelihood Estimation of Intrinsic Dimension[J].Advances in Neural Information Processing Systems 15.MIT Press,Cambridge,MA,2005:777-784.
    [173] J.N.Srivatava.An information Function Approach to Dimensionality Analysis and Curved Manifold Clustering.in Multivariate Analysis III,Academic Press,Inc.New York,,1973:369-382.
    [174] C . Francesco . Estimating the Intrinsic Dimension of Data with a Fractal-Based Method[J].IEEE Transactions on Patter Analysis and machine Intelligence,2002,24(10):1404 - 1407.
    [175] P.Mordohai.G.Medioni.Unsupervised Dimensionality Estimation and Manifold Learning in high-dimensional Spaces by Tensor Voting[C].19th Internation Joint Conference on Artifical Intelligence,Edinburgh,Scotland,2005:798-803.
    [176] J.Costa,A.O.Hero.Geodesic Entropic Graphs for Dimension and Entropy Esmation in Manifold Learning[J].IEEE Trans.on Siganl Process,2004,52(8):2210-2221.
    [177] J.Costa,A.Girotra,A.O.Hero.Estimating Local Intrinsic Dimension with K-Nearest Neighbor Graphs[J].IEEE Workshop on Statistical Signal Processing(SSP),Bordeaux,2005:417-422.
    [178]吴玲达,贺玲,蔡益朝.一种降维新方法[J].计算机应用研究,2007,24(3):89-90,93.
    [179]贺玲,吴玲达,蔡益朝.一种面向大规模图像库的降维索引新方法[J].计算机工程,2006,32(22):20-22.
    [180]李清勇.视觉感知的稀疏编码理论及其应用研究[D] .中国科学院研究生院(计算技术研究所),2006.
    [181] Lew,M.S.,Sebe,N.,Djeraba,C.,Jain,R..Content-based multimedia information retrieval : State of the art and challenges[J] . ACM Trans . Multimedia Comput.Commun.Applicat,2006,2 (1),1–19.
    [182] Neumann,D.,Gegenfurtner,K.R..Image retrieval and perceptual similarity[J].ACM Trans.Appl,2006,3 (1),31-47.
    [183] Li,B.,Chang,E.Y..Discovery of a perceptual distance function for measuring image similarity[J].Multimedia Systems,2003,8 (6),512-522.
    [184] Do,M.N.,Vetterli,M..Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J].IEEE Trans.Image Process,2002,11(2),146-158.
    [185] Pass,G.,Zabih,R..Histogram refinement for content-based image retrieval[C].Proceedings of the 1996 3rd IEEE Workshop on Applications of Computer Vision,WACV'96,Sarasota,FL,USA,1996:96-102.
    [186] Haralick,R.,Shanmugam,K.,Distein,I..Textual features for image classification[J].IEEE Trans.Systems Man Cybernet,1973,3,610-621.
    [187] Ayala,G.,Domingo,J..Spatial size distributions.Applications to shape and texture analysis[J].IEEE Trans.Pattern Anal.Machine Intell,2001,23 (12),1430-1442.
    [188] Chuang,G.,Kuo,C..Wavelet descriptor of planar curves:Theory and applications[J].IEEETrans.Image Process,1996,5 (1),56-70.
    [189] Khotanzad,A.,Hong,Y.H..Invariant image recognition by zernike moments[J].IEEE Trans.Pattern Anal.Machine Intell,1990,12 (5),489-497.
    [190] Rubner,Y.,Puzicha,J.,Tomasi,C.,Buhmann,J.M..Empirical evaluation of dissimilarity measures for color and texture[J].Computer Vision Image Understanding,2001,84 (1),25-43.
    [191] Kamarainen,J.-K.,Kyrki,V.,Ilonen,J.,KSlviSinen,H..Improving similarity measures of histograms using smoothing projections[J].Pattern Recognition Lett,2003,24 (12),2009-2019.
    [192] Long , F ., Zhang , H ., Feng , D .. Multimedia Information Retrieval and Management[M].Technological Fundamentals and Applications.Springer-Verlag,Berlin,Heidelberg,New York.Ch.Fundamentals of content-based image retrieval,2003:1-26.
    [193] Miguel Arevalillo-Herráez,Juan Domingo,Francesc J.Ferri.Combining similarity measures in content-based image retrieval[J].Pattern Recognition Letters,2008,29(16):2174-2181.
    [194]范自柱,刘二根.一种新的颜色信息熵图像检索方法[J].计算机应用研究,2008,25(1):281-282.
    [195] S.Kullback and RA Leibler.On information and sufficiency[J].The Annals of Mathematical Statistics,22(1):79-86,1951.
    [196] Zachary J M . An Information Theoretic Approach to Content Based image Retrieval[D].Louisiana State University Doctoral Dissertation,2000.
    [197] Zachary,John,Iyengar,S.S.,Barhen,Jacob .Content based image retrieval and information theory:A general approach[J].Journal of the American Society for Information Science and Technology,2001,52(10):840-852.
    [198]石莹,何炎祥,刘茂福.一种基于交互式遗传算法的图像检索模型[J] .计算机工程,2006,32(7):207-209.
    [199] chinaz.2008.Google有多少服务器答案是至少两千万台.[EB/01].http://www.ccw.com.cn /fortune/news/online_media/htm2008/20081225_567802.shtml.2008-12-25.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700