面向图像的垂直搜索引擎关键技术研究
|
摘要
随着网络带宽和软硬件性能的不断提高,多媒体文件的搜索需求日益高涨,图像检索作为文本检索到媒体检索跨域的第一步,逐渐成为是一个热门的研究领域。本文针对图像检索领域,从基于图像上下文的搜索和基于图像内容检索两方面进行研究。
首先,研究了基于图像上下文垂直搜索引擎中的网络蜘蛛技术进行网络蜘蛛抓取和主题判定,使用DOM树和正则表达式对采集后的信息进行上下文提取,对提取内容切词建立索引,在满足互联网应用的同时,利用移动互联网技术,将内容转化至移动终端,并使用树状表示法建立规则知识库,实现结果的个性化推荐。
然后,研究了基于内容的图像检索技术,将图像分层分割后,提取图像颜色、纹理和边缘特征进行多种特征融合,建立基于模糊支持向量机的多项分类模型,通过模型对图像不同图层的分类将多个关键字分配至该图像,使图像内容变成多关键字的描述
最后,在图像检索的基础上,提取图像视觉内容特征中的纹理和颜色,抓住图像间的内容特点改进色彩传递方法。一方面,通过计算图像间的纹理相似度,搜索内容与灰度图像相近的色源图像,提高染色的成功率,另一方面提取颜色特征对图像进行更科学的像素采样,改进色彩传递算法本身,加强染色效果。
With the improving performance of internet bandwidth, software and hardware, the requirement of multimedia data searching is increasing also. Since image retrieval is the first step to cross from text retrieval to multimedia retieval, it has already been a hot research field. This paper focuses on image retrieval field and researches both context based Image Retrieval and content based image retrieval.
Firstly, a web spider of context image retrieval from internet is studied to grab web page and defining the domain topic. DOM tree and Regular Expression is used to extract context, then the context is segmentted and indexed. At the same time, a mobile module is added in this search engine to propose an implementation method of mobile search engine, and a knowledge-based tree-like representation is introduced to present individually recommend.
Secondly, content based image retrieval is researched. Image is divided into the multi-level regions. For each of them, the combined feature is extracted and inputted into the trained Fuzzy SVMs to classify, which has been proved better than conventional SVMs in the generalization ability. The values of classification in each category are calculated. Based on these values, the keywords are assigned.
At last, visble features of image texture and color are extracted as indexes to catch the content characters of images based on the retrieval. They are used to transfer color from a source image to a target image, which builds a pixels relationship between the two images to render color. For one thing, the texture feature is used to compute the similarity between colorful image and grayscale image to search a similar source image which can help to increase the successful possibility of rendering. For another, the color feature is extracted to use to select sample pixels from source image to improve the rendering effect.
首先,研究了基于图像上下文垂直搜索引擎中的网络蜘蛛技术进行网络蜘蛛抓取和主题判定,使用DOM树和正则表达式对采集后的信息进行上下文提取,对提取内容切词建立索引,在满足互联网应用的同时,利用移动互联网技术,将内容转化至移动终端,并使用树状表示法建立规则知识库,实现结果的个性化推荐。
然后,研究了基于内容的图像检索技术,将图像分层分割后,提取图像颜色、纹理和边缘特征进行多种特征融合,建立基于模糊支持向量机的多项分类模型,通过模型对图像不同图层的分类将多个关键字分配至该图像,使图像内容变成多关键字的描述
最后,在图像检索的基础上,提取图像视觉内容特征中的纹理和颜色,抓住图像间的内容特点改进色彩传递方法。一方面,通过计算图像间的纹理相似度,搜索内容与灰度图像相近的色源图像,提高染色的成功率,另一方面提取颜色特征对图像进行更科学的像素采样,改进色彩传递算法本身,加强染色效果。
With the improving performance of internet bandwidth, software and hardware, the requirement of multimedia data searching is increasing also. Since image retrieval is the first step to cross from text retrieval to multimedia retieval, it has already been a hot research field. This paper focuses on image retrieval field and researches both context based Image Retrieval and content based image retrieval.
Firstly, a web spider of context image retrieval from internet is studied to grab web page and defining the domain topic. DOM tree and Regular Expression is used to extract context, then the context is segmentted and indexed. At the same time, a mobile module is added in this search engine to propose an implementation method of mobile search engine, and a knowledge-based tree-like representation is introduced to present individually recommend.
Secondly, content based image retrieval is researched. Image is divided into the multi-level regions. For each of them, the combined feature is extracted and inputted into the trained Fuzzy SVMs to classify, which has been proved better than conventional SVMs in the generalization ability. The values of classification in each category are calculated. Based on these values, the keywords are assigned.
At last, visble features of image texture and color are extracted as indexes to catch the content characters of images based on the retrieval. They are used to transfer color from a source image to a target image, which builds a pixels relationship between the two images to render color. For one thing, the texture feature is used to compute the similarity between colorful image and grayscale image to search a similar source image which can help to increase the successful possibility of rendering. For another, the color feature is extracted to use to select sample pixels from source image to improve the rendering effect.
引文
[1]Shen H. T, Ooi B. C, Tan K. L. Giving meanings to www images. Proceedings of ACM Multimedia, Los Angeles,CA,USA,2000:39-48.
[2]Rui Y, Huang T. S, Ortega M. Relevance Feedback:A Power Tool for Interactive Content-Based Image Retrieval, IEEE Transactions on Circuits and Systems for Video Technology,1998,8(5): 644-655.
[3]Elworthy D. Retrieval from Captioned Image Databases using Natural Language Processing. In Proc.9th Conf. on Information and Knowledge Management Pages, McLean, VA,2000:430-437.
[4]Ekains J. P,Garham M. E. Content Based Image Retrieval. JISC Technology Application Program, Newcastle upon Tyne,2000:253-261.
[5]Christian B, Stefan B, Daniel A. Searching in high-dimensional spaces:Index structures for improving the performance of multimedia databases. ACM Computing Surveys(CSUR), 2001,33(3):322-373
[6]Smeulders A.W. M, Worring M.,Santini S et al. Content-based image retrieval at the end of the early years. IEEE Transaction on Pattern Analysis and Machine Intelligence,2000, 22 (12):1349-1380.
[7]Rui Y, HuangQ. Image Retrieval:Current Techniques, Promising Directions and Open Issues. Journal of Visual Communication and Image Representation,1999,10(3):39-62.
[8]Rui Y, Thomas S. H, Chang S. F. Image Retrieval:Past, Present and Future. Symposium on Multimedia Information Processing,1997:108-116.
[9]Flickner M, Sawhney H, Niblack W. Query by image and video content:The QBIC system. IEEE Computer,1995,28(9):23-32.
[10]Bach J, Fuller C, Gupta A. The virage image search engin:An open framework for image management. Proceedings of Storage and Retrieval for Image and Video Databases, San Diego/La Jolla.CA, USA,1996.-76-87.
[11]Pentland A, Picard R. W, Sclaroff S. Photobook:Content-based manipulation of image database. International Journal of Computer Vision,1996,18(3):233-254.
[12]温超,耿国华.基于内容图像检索中的“语义鸿沟”问题.西北大学学报(自然科学版),2005,35(5)：536540.
[13]Feng J, Ming J. L et al. An Efficient and Effective Region-Based Image Retrieval Framework. IEEE Trans. Image Processing,2004,13(5):699-710.
[14]罗沄,章毓晋,高永英.基于分析的图像有意义区域提取.计算机学报,2000,23(12)：1313-1319.
[15]Sheikholeslami G, Chang W, Zhang A. Semantic clustering and querying on heterogeneous features for visual data. In:Proc. of the ACM Multimedia, Bristol, England,1998:3-12.
[16]魏维,游静,刘凤玉等.语义视频检索综述.计算机科学,2006,33(2)：1-7.
[17]王崇骏,杨育彬,陈世福.基于高层语义的图像检索算法.软件学报,2004,15(10)：1461-1469.
[18]Sanghoon L, Melba M. C. Unsupervised Multistage Image Classification Using Hierarchical Clustering With a Bayesian Similarity Measure. IEEE Transaction on Image Processing, 2005,14(3):312-320.
[19]Fung C Y, Loe F K. Learning primitive and scene semantics of images for classification and retrieval. In:Proc. of the ACM Multimedia, Orlando,1999:9-12.
[20]朱兴全.iFind:一个结合语义和视觉特征的图像相关回馈检索系统.计算机学报,2002,3(7)：681-689.
[21]Zhao R, Grosky W I. Narrowing semantic gap improved text-based web document retrieval using visual features. IEEE Trans. Multimedia,2002,4(2):189-200.
[22]包燕晗.搜索引擎存在的问题与发展趋势.中国信息导报,2006,(4)：62-63.
[23]朱茂盛,王斌.元搜索引擎及其实现,计算机工程,2002,28(10)：10-13.
[24]陈新颜.垂直搜索引擎辨析.现代情报,2004,(9)：133-144.
[25]赵志荣.垂直网站与垂直搜索.中国信息导报,2000,(11)：18-19.
[26]李明斐,汲业.基于知识库的e-Learning模型,中国管理学,2004,12 special：478-481.
[27]寿周翔.专业搜索引擎的研究与设计：(硕士学位论文).杭州：浙江大学,2005.
[28]刘畅.综合搜索引擎与垂直搜索引擎的比较研究.情报科学,2007,25(1)：97-102.
[29]肖东梅.垂直搜索引擎研究.图书馆学研究,2003,(2)：87-89.
[30]化柏林.搜索引擎面面观.中国计算机用户.2004,(25)：49.
[31]Soumen Chakrabarti, Martin van den Berg, Byron Dom.Focused crawling:a new approach to topic specific web resource discovery. Computer Networks,1999,31(11-16):1623-1640.
[32]Cho J,Garcia-Molina H, Page L. Efficient Crawling through URL Ordering. Computer Networks,1998,30(1-7):161-172.
[33]Bharat K, Henznger M. Improved Algorithms for Topic Distillation in a Hyperlinked Environment. Proceeding of SIGIR Conference on Research and Development in Information Retrieval, New York, USA,1998:104-111.
[34]钱功伟,倪林.基于网页链接和内容分析的改进PageRank算法.计算机工程与应用,2007,43(21)：160-164.
[35]Ayma A. Issa, Munib A. Qutaishat, Dynamic Multiplatform HTML to WML Mobile Converter [C]. Proceedings of the World Congress on Engineering 2008 Vol I. London,2008:816-821.
[36]S e bastien Aperghis Tramoni, Html2Wml. http://htmlwml.sourceforge.net/index.html.
[37]张成洪,古晓洪,白延红.Web数据抽取技术研究进展.计算机科学,2004,31(2)：129-131.
[38]Soderland S. Learning Information Extraction Rules for Semi-structured and Free Text. Machine Learning,1999,34(1-3):233-272.
[39]Califf M, Mooney R.Relational Learning of pattern-match rules for information extract ion. The Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence, Orlando,Florida,1999:328-334.
[40]HSU C N, DUNG M. Generating finite-state transducers for semi-structured data extraction from the Web. Information System,1998,23(8),521-538.
[41]Muslea I,Minton S, Knolock C. Hierarchical wrapper induction for semi-structured information sources. Autonomous Agents and Multi-agent Systems,2001,4(1-2):93-114.
[42]周登朋,谢康林.Lucene搜索引擎.计算机工程,2007,33(18)95-96.
[43]Han J, Fu Y. Discovery of multiple-level association rules from large databases. Proceedings of the VLDB Conference,Zurich, Switzerland,1995:420-431.
[44]Bing Liu, Minqing Hu, Wynne Hsu.Multi-level organization and summarization of the discovered rules. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston,USA,2000:208-217.
[45]Robert Baumgartner, SergioFlesca, Georg Gottlob. Visual web information extraction with lixto. Proceedings of the 27th VLDB Conference. Roma, Italy,2001:27-36.
[46]A Finn, A Kushmerick, B Smyth. Fact or fiction:Content classification for digital libraries. The 2nd DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries, Dublin, Ireland,2001.
[47]E Kaasinen, M Aaltonen, J Kolari, et all. Two approaches to bringing Internet services to WAP devicesl In:Proc of the 9th Int. World Wide Web Conf on Computer Networks Amsterdam North-Hollan d Publishing Co,2000 1231-246.
[48]0 Buyukkokten, H Garcia-Molina, A Paepckel Seeing the w hole in part s:Text summarization for Web brow sing on handh eld devicesl In:Proc of the 10th Int.1 Conf on World WideWebl New York:ACM Press,20011 652-662.
[49]S Gupt a, G Kaiser, D Neistadt, et all. DOM-based content extract ion of HTML documentsl In:Proc of the 12th Int. World Wide Web Conf1 New York:ACM Press,20031 207-214.
[50]李效东,顾毓清.基于DOM的Web信息提取.计算机学报,2002,25(5)：526-533.
[51]刘迁,贾惠波.中文信息处理中自动分词技术的研究与展望.计算机工程与应用,2006,(3)：175-177.
[52]Choi A, Cheng C H, Ko Y L. Word extraction from Chinese documents by occurrence counts[A].1988 International Conference on Computer Processing of Chinese and Oriental Languages, Toronto, Canada:488-491.
[53]Fan C K, Tsai W H. Automatic word identification in Chinese sentences by the relaxation technique. Computer Processing of Chinese and Oriental Languages,1988,4(1):33-56.
[54]郭辉,苏中义,王文等.一种改进的MM分词算法.微型电脑应用.2002,18(1)：13-15.
[55]李庆虎,陈玉健,孙家广.一种中文分词词典新机制—双字哈希机制.中文信息学报,2002,17(4)：13-18.
[56]薛菘.基于Web数据库平台的图书馆个性化服务：MyLibrary.图书情报工作,2002,(8)：22-25.
[57]史田华.因特网个性化信息服务.情报资料工作,2002,(1)：31-32,38.
[58]Mobasher B, Dai H, Luo T et al.Effective personalization based on association rule discovery from web usage data.3rd international workshop on Web information and data management, Atlanta,2001:9-15.
[59]Joachims T, Freitag D,Mitchell T. WebWatcher:a tour guide for the World Wide Web. International Joint Conference on Artificial Intelligence, San Francisco,1997: 770-777.
[60]安芳.电子商务个性化信息推荐服务的研究：(硕士学位论文).北京：对外经济贸易大学,2006.
[61]冯翱,刘斌,卢增祥等.Open Bookmark——基于Agent的信息过滤系统.清华大学学报(自然科学版),2001,41(3)：85-88.
[62]张灵玲,周文辉,韩耀伟等.基于Internet的课件信息发现和收集Agent的研究.计算机研究与发展,1999,36(4)：465-471.
[63]揭春雨,刘源,梁南元.论汉语自动分词方法.中文信息学报,1989,3(1)：1-9
[64]黄崑,符绍宏.自动分词技术及其在信息检索中应用的研究.现代图书情报技术,2001,(3)：2629
[65]孙茂松,左正平,黄昌宁.汉语自动分词词典机制的实验研究.中文信息学报,2000,14(1)：1-6
[66]陈嘉玫.WAP闸道器上的HTML过滤器之设计：(硕士学位论文).台湾：“国立”中山大学资讯管理研究所,2001.
[67]陈明,孙丽丽.基于WAP的移动搜索模型,计算机工程,2008,3(34)：205-209.
[68]胡祥培,修立军,杨德礼.生产作业计划问题的知识表示研究.管理工程学报,1999,2(2)：41-48.
[69]胡祥培,钱国明,胡运权.运筹学规划问题一种基于知识的树状表示法.哈尔滨工业大学学报,1997,29(3)：8-11.
[70]黄祥林,沈兰荪.基于内容的图像检索技术研究.电子学报,2002,30(7)：1066-1071.
[71]Manjunath B S, Ma W Y. Texture features for browsing of image data. IEEE Transactions On PAMI,1996,18(8):837-842.
[72]夏良正,陈延标,李久贤.数字图像处理.南京：东南大学出版社,1998.
[73]刘未来.基于内容的图像检索方法与实现：(硕士学位论文).上海：华东理工大学,2010.
[74]刘忠伟,章毓晋.十种基于颜色特征的图像检索算法的比较和分析.信号处理,2000,16(1)：79-84.
[75]章毓晋.图像处理与分析技术.北京：高等教育出版社,2008.
[76]章毓晋.基于内容的视觉信息检索.北京：科学出版社.2003.
[77]丁险峰,吴洪,张宏江等.形状匹配综述.自动化学报,2001,27(5)：678—694.
[78]范自柱.基于内容的图像检索技术综述.华东交通大学学报,2005,22(5)：147150.
[79]郭丽娟,孙世宇,段修生.支持向量机及核函数研究.科学技术与工程,2008,8(2)：487-490.
[80]唐立军,段立娟.基于内容的图像检索系统.计算机应用研究,2001,7(18)：41-45.
[81]韦娜,耿国华,周明全.基于内容的图像检索系统性能评价.中国图像图形学报,2004,9(11)：1271-1276.
[82]陈素玲.基于粗糙集理论的图像检索：(硕士学位论文).昆明：昆明理工大学,2005.
[83]Liu Y, Zhang D, Lu G et al.A survey of content-based image retrieval with high-level semantics. Pattern Recognition,2007,40(1):262-282.
[84]Stricker M, Orengo M. Similarity of color images. SPIE Storage Retrieval Image Video Databases Ⅲ,1995,2(2185):381-392.
[85]章毓晋.图像工程(上册)图像处理与分析.北京：清华大学出版社,1999.
[86]Smith J R, Chang S F. Tools and Techniques for Color Image Retrieval. Proceedings of SPIE Storage and Retrieval for Image and Video Databases, San Diego, CA, USA,1995:1630-1639.
[87]王镜.基于颜色特征的图像检索方法技术研究：(硕士学位论文).北京：首都经济贸易大学,2006.
[88]Huang J, Kumar S, Mitra M et al. Image Indexing Using Color Correlogram. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Providence, RI.USA,1997:762-768.
[89]Lew M, Sebe N, Djeraba C. Content-based multimedia information retrieval:State of the art and challenges. ACM Transactions on Multimedia Computing, Communications, and Applications(TOMCCAP),2006,2(1):1-19.
[90]Zhang D, Wong A, Indrawan M et al. Content-Based Image Retrieval Using Gabor Texture Features. Proc. Pacific-Rim Conference on Multimedia, Sydney, Australia,2000:392-395.
[91]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.计算机学报,2005,28(12)：19691978.
[92]张元清,包骏杰,况夯等.基于贝叶斯理论的图像标注和检索.计算机科学,2008,35(8)：229231.
[93]Pass G, Zabih R, Miller J. Comparing Images Using Color Coherence Vectors. Proceedings of ACM International Conference on Multimedia, Boston, USA,1996:65-73.
[94]Hafner J, Sawhney H. Efficient color histogram indexing for quadratic form distance functions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1995, 17(7):729-736.
[95]Han J, Ma K K. Fuzzy color histogram:An efficient color feature for image indexing and retrieval. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada,2000:2011-2014.
[96]WAN H. L, Morshed U. C. Image Semantic Classification by Using SVMs, Journal of Software, 2003,14 (11):1891-1899.
[97]何清法,李国杰.综合分块主色和相关反馈技术的图像检索方法.计算机辅助设计与图形学学报,2001,13(10)：912-917.
[98]邬浩,潘云鹤,庄越挺.基于对象形状的图像查询技术.软件学报,1998,9(5)：344349.
[99]Lebrun J, Foliguet S, Gosselin P. Image retrieval with graph kernel on regions. Proceedings of International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008:1-4.
[100]周明全,耿国华,韦娜.基于内容图像检索技术.北京：清华大学出版社,2007.
[101]陈允杰,张建伟,韦志辉,王平安.基于高斯混合模型的活动轮廓模型脑MRI分割.计算机研究与发展,2007,44(9)：1595-1603.
[102]李清勇,胡宏,施智平.基于纹理语义特征的图像检索研究.计算机学报,2006,29(1)：116-122.
[103]高中义.基于形状特征的图像分析算法研究：(硕士学位论文).长春：吉林大学,2002.
[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.计算机研究与发展,2003,40(11)：15661570.
[105]王文惠,周良柱,万建伟.基于内容的图像检索技术的研究和发展.计算机工程与应用,2001,37(5)：54-55.
[106]陈纯.计算机图像处理技术与算法.北京：清华大学出版社,2003.
[107]李弼程,彭天强,彭波.智能图像处理技术.北京：电子工业出版社,2004.
[108]孙兴华,郭丽,郭跃飞.基于目标区域的彩色图像检索研究.计算机研究与发展,2001,38(9)：11121119.
[109]张磊,林福宗,张钹.基于支持向量机的相关反馈图像检索算法.清华大学学报,2002,42(1)：8083.
[110]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机.北京：科学出版社,2004.
[111]李向阳,庄越挺,潘云鹤.基于内容的图像检索技术与系统.计算机研究与发展,2001,38(3)：344-354.
[112]李鹅杰,杨树元.一种基于内容的图像检索系统.微计算机应用,2001,22(3)：139-141.
[113]王亮申.图像特征提取及基于内容图像数据库检索理论和方法研究：(硕士学位论文).大连：大连理工大学,2002.
[114]张玲.基于内容的图像检索方法技术研究：(硕士学位论文).北京：北京化工大学,2005.
[115]张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1)：3242.
[116]Tsujinishi D, Abe S. Fuzzy least squares support vector machines for multi-class problems. Neural Networks,2003,16(5):785-792.
[117]付岩,王耀威,王伟强.SVM用于基于内容的自然图像分类和检索.计算机学报,2003,26(10)：1261—1265.
[118]Shi J, Malik J. Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[119]Derin H, Elliott H, Cristi R et al. Bayes smoothing algorithms for segmentation of binary images modeled by markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):707-720.
[120]Lim Y. W, Lee S. U. On the color image segmentation algorithm based on the thresholding and fuzzy c-means techniques. Pattern Recognition,1990,23(9):935-952.
[121]Prager J. M. Extracting and labeling boundary segments in natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence,1980,2(1):16-27.
[122]Inoue T, Abe S. Fuzzy support vector machines for pattern classification. In Proceedings of International.Joint Conference on Neural Networks, Washington,2001: 1449-1454.
[123]Lin C. F, Wang S. D. Fuzzy support vector machines. IEEE Transactions on Neural Networks, 2002,13(2):464-471.
[124]Efros A A, Freeman W T. Image quilting for texture synthesis and transfer. Proceedings of the 28th annual conference on Computer graphics and interactive techniques. ACM,2001: 341-346.
[125]Efros A A, Leung T K. Texture synthesis by non-parametric sampling. Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on. IEEE,1999,2: 1033-1038.
[126]汲业,陈燕,牟向伟,屈莉莉.基于内容的图像染色算法.Computer Engineering,2012,38(20).
[127]Kekre H B, Thepade S D. Color Traits Tranfer to Grayscale Images. Proceesings of the 1st International Conference on Emerging Trends in Engieering and Technology. Nagpur, Indua,2008:82-85.
[128]Pouli T, Reinharda E. Progressive Color Transfer for Images of Arbitary Dynamic. Computer&Grapgs,2011,35(1):67-80.
[129]Pitie F, Kokarama A C, Dahyota R. Automatid Color Grading using Colour Distribution Transfer. Computer Vision and Image Understanding.2007,107(1):123-137.
[130]宋麦玲,李欢.一种基于HSV颜色空间的图像检索技术.电脑知识与技术：学术交流,2007(1)：200-200.
[131]贾云涛,胡事民.基于图切分的交互式图像染色算法.计算机学报.2006,29(3)：508-512.
[132]Tomohisa W, Ashikhmin M. Transferring Color to Greyscale Images. Proceedings of ACM SIGGRAPH'02. San Antonio, USA; ACM Press,2002:227-280.
[133]陈路,李小东.基于BP神经网络的CMY到XYZ颜色空间转换算法研究.包装工程.2007,28(7),63-64.
[134]钟克洪,丁明跃,周成平,周亚安.基于均匀空间的颜色分级方法.中国图象图形学报.2004,9(11),1277-1281.
[135]龙永红,殷进.颜色空间变换及其在印刷中的应用.湖南工业大学学报.2007,21(006)：8-10.
[136]曹从军,刘强珺.基于径向基函数神经网络的颜色空间转换研究.中国印刷与包装研究.2010,2(11)：48-51.
[137]Reinhard E, Adhikhmin M, Gooch B, et al. Color transfer between images. Computer Graphics and Applications, IEEE,2001,21(5):34-41.
[138]李申燕.图像颜色迁移技术的研究.太原：中北大学.2009.
[139]黄伟,敬忠良,李建勋,等.基于1αβ空间的多光谱和全色图像融合.2006,32(11)：22-24.
[140]袁樵,詹庆旋.国际照明委员会照明标准——室内工作场所照明.照明工程学报.2002,13(4)：55-59.
[141]王浩,曾朝阳,兰永杰.LMS颜色空间稳定性在伪装评价中的应用.光电技术应用.200722(5)：10-12.
[142]Abdi. H., Williams, L. J. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics.2010,2:433-459.
[143]Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and intelligent laboratory systems.1987,2(1):37-52.
[144]陈小娥,陈昭炯.若干色彩传递应用模式的比较研究.计算机应用.2007,27(1)：146-147.
[145]D. L. Ruderman, T. W. Cronin, and C. C. Chiao. Statistics of coneresponses to natural images:implications for visual coding. J. Optical Soc. Of America.1998,15(8),2036-2045.
[146]韩静,罗立民,鲍旭东.基于小波变换的图像染色算法.中国图象图形学报,2007,12(9)：15791584.
[147]姚若河,黄继武.改进的直方图均衡化图象增强算法.铁道学报,1997,19(6)：7881.
[148]汪志云,黄梦为,胡钋,等.基于直方图的图像增强及其MATLAB实现.计算机工程与科学,2006,28(2)：5456.
[149]武治国,王延杰.一种基于直方图非线性变换的图像对比度增强方法.光子学报,2010,39(4)：755-758.
[150]施智平,胡宏,李清勇,等.一种快速有效的图像纹理谱描述子.计算机辅助设计与图形学学报,2004,16(012)：1703-1707.
[151]宋强,徐科,徐金梧.一类新的纹理谱描述子.计算机工程,2007,33(7)：30-32.
[152]柏子游,张勇.一种彩色图像的色彩分割方法.模式识别与人工智能,1999,12(002)：241-244.
[153]伯晓晨,刘建平.基于颜色直方图的图像检索.中国图象图形学报：A辑,1999,4(1)：3337.
[154]高美真,申艳梅.基于颜色直方图的图像检索技术.微电子学与计算机,2008,25(004)：25-27.
[155]Jain A K, Vailaya A. Image retrieval using color and shape. Pattern recognition,1996, 29(8):1233-1244.
[2]Rui Y, Huang T. S, Ortega M. Relevance Feedback:A Power Tool for Interactive Content-Based Image Retrieval, IEEE Transactions on Circuits and Systems for Video Technology,1998,8(5): 644-655.
[3]Elworthy D. Retrieval from Captioned Image Databases using Natural Language Processing. In Proc.9th Conf. on Information and Knowledge Management Pages, McLean, VA,2000:430-437.
[4]Ekains J. P,Garham M. E. Content Based Image Retrieval. JISC Technology Application Program, Newcastle upon Tyne,2000:253-261.
[5]Christian B, Stefan B, Daniel A. Searching in high-dimensional spaces:Index structures for improving the performance of multimedia databases. ACM Computing Surveys(CSUR), 2001,33(3):322-373
[6]Smeulders A.W. M, Worring M.,Santini S et al. Content-based image retrieval at the end of the early years. IEEE Transaction on Pattern Analysis and Machine Intelligence,2000, 22 (12):1349-1380.
[7]Rui Y, HuangQ. Image Retrieval:Current Techniques, Promising Directions and Open Issues. Journal of Visual Communication and Image Representation,1999,10(3):39-62.
[8]Rui Y, Thomas S. H, Chang S. F. Image Retrieval:Past, Present and Future. Symposium on Multimedia Information Processing,1997:108-116.
[9]Flickner M, Sawhney H, Niblack W. Query by image and video content:The QBIC system. IEEE Computer,1995,28(9):23-32.
[10]Bach J, Fuller C, Gupta A. The virage image search engin:An open framework for image management. Proceedings of Storage and Retrieval for Image and Video Databases, San Diego/La Jolla.CA, USA,1996.-76-87.
[11]Pentland A, Picard R. W, Sclaroff S. Photobook:Content-based manipulation of image database. International Journal of Computer Vision,1996,18(3):233-254.
[12]温超,耿国华.基于内容图像检索中的“语义鸿沟”问题.西北大学学报(自然科学版),2005,35(5)：536540.
[13]Feng J, Ming J. L et al. An Efficient and Effective Region-Based Image Retrieval Framework. IEEE Trans. Image Processing,2004,13(5):699-710.
[14]罗沄,章毓晋,高永英.基于分析的图像有意义区域提取.计算机学报,2000,23(12)：1313-1319.
[15]Sheikholeslami G, Chang W, Zhang A. Semantic clustering and querying on heterogeneous features for visual data. In:Proc. of the ACM Multimedia, Bristol, England,1998:3-12.
[16]魏维,游静,刘凤玉等.语义视频检索综述.计算机科学,2006,33(2)：1-7.
[17]王崇骏,杨育彬,陈世福.基于高层语义的图像检索算法.软件学报,2004,15(10)：1461-1469.
[18]Sanghoon L, Melba M. C. Unsupervised Multistage Image Classification Using Hierarchical Clustering With a Bayesian Similarity Measure. IEEE Transaction on Image Processing, 2005,14(3):312-320.
[19]Fung C Y, Loe F K. Learning primitive and scene semantics of images for classification and retrieval. In:Proc. of the ACM Multimedia, Orlando,1999:9-12.
[20]朱兴全.iFind:一个结合语义和视觉特征的图像相关回馈检索系统.计算机学报,2002,3(7)：681-689.
[21]Zhao R, Grosky W I. Narrowing semantic gap improved text-based web document retrieval using visual features. IEEE Trans. Multimedia,2002,4(2):189-200.
[22]包燕晗.搜索引擎存在的问题与发展趋势.中国信息导报,2006,(4)：62-63.
[23]朱茂盛,王斌.元搜索引擎及其实现,计算机工程,2002,28(10)：10-13.
[24]陈新颜.垂直搜索引擎辨析.现代情报,2004,(9)：133-144.
[25]赵志荣.垂直网站与垂直搜索.中国信息导报,2000,(11)：18-19.
[26]李明斐,汲业.基于知识库的e-Learning模型,中国管理学,2004,12 special：478-481.
[27]寿周翔.专业搜索引擎的研究与设计：(硕士学位论文).杭州：浙江大学,2005.
[28]刘畅.综合搜索引擎与垂直搜索引擎的比较研究.情报科学,2007,25(1)：97-102.
[29]肖东梅.垂直搜索引擎研究.图书馆学研究,2003,(2)：87-89.
[30]化柏林.搜索引擎面面观.中国计算机用户.2004,(25)：49.
[31]Soumen Chakrabarti, Martin van den Berg, Byron Dom.Focused crawling:a new approach to topic specific web resource discovery. Computer Networks,1999,31(11-16):1623-1640.
[32]Cho J,Garcia-Molina H, Page L. Efficient Crawling through URL Ordering. Computer Networks,1998,30(1-7):161-172.
[33]Bharat K, Henznger M. Improved Algorithms for Topic Distillation in a Hyperlinked Environment. Proceeding of SIGIR Conference on Research and Development in Information Retrieval, New York, USA,1998:104-111.
[34]钱功伟,倪林.基于网页链接和内容分析的改进PageRank算法.计算机工程与应用,2007,43(21)：160-164.
[35]Ayma A. Issa, Munib A. Qutaishat, Dynamic Multiplatform HTML to WML Mobile Converter [C]. Proceedings of the World Congress on Engineering 2008 Vol I. London,2008:816-821.
[36]S e bastien Aperghis Tramoni, Html2Wml. http://htmlwml.sourceforge.net/index.html.
[37]张成洪,古晓洪,白延红.Web数据抽取技术研究进展.计算机科学,2004,31(2)：129-131.
[38]Soderland S. Learning Information Extraction Rules for Semi-structured and Free Text. Machine Learning,1999,34(1-3):233-272.
[39]Califf M, Mooney R.Relational Learning of pattern-match rules for information extract ion. The Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence, Orlando,Florida,1999:328-334.
[40]HSU C N, DUNG M. Generating finite-state transducers for semi-structured data extraction from the Web. Information System,1998,23(8),521-538.
[41]Muslea I,Minton S, Knolock C. Hierarchical wrapper induction for semi-structured information sources. Autonomous Agents and Multi-agent Systems,2001,4(1-2):93-114.
[42]周登朋,谢康林.Lucene搜索引擎.计算机工程,2007,33(18)95-96.
[43]Han J, Fu Y. Discovery of multiple-level association rules from large databases. Proceedings of the VLDB Conference,Zurich, Switzerland,1995:420-431.
[44]Bing Liu, Minqing Hu, Wynne Hsu.Multi-level organization and summarization of the discovered rules. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston,USA,2000:208-217.
[45]Robert Baumgartner, SergioFlesca, Georg Gottlob. Visual web information extraction with lixto. Proceedings of the 27th VLDB Conference. Roma, Italy,2001:27-36.
[46]A Finn, A Kushmerick, B Smyth. Fact or fiction:Content classification for digital libraries. The 2nd DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries, Dublin, Ireland,2001.
[47]E Kaasinen, M Aaltonen, J Kolari, et all. Two approaches to bringing Internet services to WAP devicesl In:Proc of the 9th Int. World Wide Web Conf on Computer Networks Amsterdam North-Hollan d Publishing Co,2000 1231-246.
[48]0 Buyukkokten, H Garcia-Molina, A Paepckel Seeing the w hole in part s:Text summarization for Web brow sing on handh eld devicesl In:Proc of the 10th Int.1 Conf on World WideWebl New York:ACM Press,20011 652-662.
[49]S Gupt a, G Kaiser, D Neistadt, et all. DOM-based content extract ion of HTML documentsl In:Proc of the 12th Int. World Wide Web Conf1 New York:ACM Press,20031 207-214.
[50]李效东,顾毓清.基于DOM的Web信息提取.计算机学报,2002,25(5)：526-533.
[51]刘迁,贾惠波.中文信息处理中自动分词技术的研究与展望.计算机工程与应用,2006,(3)：175-177.
[52]Choi A, Cheng C H, Ko Y L. Word extraction from Chinese documents by occurrence counts[A].1988 International Conference on Computer Processing of Chinese and Oriental Languages, Toronto, Canada:488-491.
[53]Fan C K, Tsai W H. Automatic word identification in Chinese sentences by the relaxation technique. Computer Processing of Chinese and Oriental Languages,1988,4(1):33-56.
[54]郭辉,苏中义,王文等.一种改进的MM分词算法.微型电脑应用.2002,18(1)：13-15.
[55]李庆虎,陈玉健,孙家广.一种中文分词词典新机制—双字哈希机制.中文信息学报,2002,17(4)：13-18.
[56]薛菘.基于Web数据库平台的图书馆个性化服务：MyLibrary.图书情报工作,2002,(8)：22-25.
[57]史田华.因特网个性化信息服务.情报资料工作,2002,(1)：31-32,38.
[58]Mobasher B, Dai H, Luo T et al.Effective personalization based on association rule discovery from web usage data.3rd international workshop on Web information and data management, Atlanta,2001:9-15.
[59]Joachims T, Freitag D,Mitchell T. WebWatcher:a tour guide for the World Wide Web. International Joint Conference on Artificial Intelligence, San Francisco,1997: 770-777.
[60]安芳.电子商务个性化信息推荐服务的研究：(硕士学位论文).北京：对外经济贸易大学,2006.
[61]冯翱,刘斌,卢增祥等.Open Bookmark——基于Agent的信息过滤系统.清华大学学报(自然科学版),2001,41(3)：85-88.
[62]张灵玲,周文辉,韩耀伟等.基于Internet的课件信息发现和收集Agent的研究.计算机研究与发展,1999,36(4)：465-471.
[63]揭春雨,刘源,梁南元.论汉语自动分词方法.中文信息学报,1989,3(1)：1-9
[64]黄崑,符绍宏.自动分词技术及其在信息检索中应用的研究.现代图书情报技术,2001,(3)：2629
[65]孙茂松,左正平,黄昌宁.汉语自动分词词典机制的实验研究.中文信息学报,2000,14(1)：1-6
[66]陈嘉玫.WAP闸道器上的HTML过滤器之设计：(硕士学位论文).台湾：“国立”中山大学资讯管理研究所,2001.
[67]陈明,孙丽丽.基于WAP的移动搜索模型,计算机工程,2008,3(34)：205-209.
[68]胡祥培,修立军,杨德礼.生产作业计划问题的知识表示研究.管理工程学报,1999,2(2)：41-48.
[69]胡祥培,钱国明,胡运权.运筹学规划问题一种基于知识的树状表示法.哈尔滨工业大学学报,1997,29(3)：8-11.
[70]黄祥林,沈兰荪.基于内容的图像检索技术研究.电子学报,2002,30(7)：1066-1071.
[71]Manjunath B S, Ma W Y. Texture features for browsing of image data. IEEE Transactions On PAMI,1996,18(8):837-842.
[72]夏良正,陈延标,李久贤.数字图像处理.南京：东南大学出版社,1998.
[73]刘未来.基于内容的图像检索方法与实现：(硕士学位论文).上海：华东理工大学,2010.
[74]刘忠伟,章毓晋.十种基于颜色特征的图像检索算法的比较和分析.信号处理,2000,16(1)：79-84.
[75]章毓晋.图像处理与分析技术.北京：高等教育出版社,2008.
[76]章毓晋.基于内容的视觉信息检索.北京：科学出版社.2003.
[77]丁险峰,吴洪,张宏江等.形状匹配综述.自动化学报,2001,27(5)：678—694.
[78]范自柱.基于内容的图像检索技术综述.华东交通大学学报,2005,22(5)：147150.
[79]郭丽娟,孙世宇,段修生.支持向量机及核函数研究.科学技术与工程,2008,8(2)：487-490.
[80]唐立军,段立娟.基于内容的图像检索系统.计算机应用研究,2001,7(18)：41-45.
[81]韦娜,耿国华,周明全.基于内容的图像检索系统性能评价.中国图像图形学报,2004,9(11)：1271-1276.
[82]陈素玲.基于粗糙集理论的图像检索：(硕士学位论文).昆明：昆明理工大学,2005.
[83]Liu Y, Zhang D, Lu G et al.A survey of content-based image retrieval with high-level semantics. Pattern Recognition,2007,40(1):262-282.
[84]Stricker M, Orengo M. Similarity of color images. SPIE Storage Retrieval Image Video Databases Ⅲ,1995,2(2185):381-392.
[85]章毓晋.图像工程(上册)图像处理与分析.北京：清华大学出版社,1999.
[86]Smith J R, Chang S F. Tools and Techniques for Color Image Retrieval. Proceedings of SPIE Storage and Retrieval for Image and Video Databases, San Diego, CA, USA,1995:1630-1639.
[87]王镜.基于颜色特征的图像检索方法技术研究：(硕士学位论文).北京：首都经济贸易大学,2006.
[88]Huang J, Kumar S, Mitra M et al. Image Indexing Using Color Correlogram. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Providence, RI.USA,1997:762-768.
[89]Lew M, Sebe N, Djeraba C. Content-based multimedia information retrieval:State of the art and challenges. ACM Transactions on Multimedia Computing, Communications, and Applications(TOMCCAP),2006,2(1):1-19.
[90]Zhang D, Wong A, Indrawan M et al. Content-Based Image Retrieval Using Gabor Texture Features. Proc. Pacific-Rim Conference on Multimedia, Sydney, Australia,2000:392-395.
[91]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.计算机学报,2005,28(12)：19691978.
[92]张元清,包骏杰,况夯等.基于贝叶斯理论的图像标注和检索.计算机科学,2008,35(8)：229231.
[93]Pass G, Zabih R, Miller J. Comparing Images Using Color Coherence Vectors. Proceedings of ACM International Conference on Multimedia, Boston, USA,1996:65-73.
[94]Hafner J, Sawhney H. Efficient color histogram indexing for quadratic form distance functions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1995, 17(7):729-736.
[95]Han J, Ma K K. Fuzzy color histogram:An efficient color feature for image indexing and retrieval. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada,2000:2011-2014.
[96]WAN H. L, Morshed U. C. Image Semantic Classification by Using SVMs, Journal of Software, 2003,14 (11):1891-1899.
[97]何清法,李国杰.综合分块主色和相关反馈技术的图像检索方法.计算机辅助设计与图形学学报,2001,13(10)：912-917.
[98]邬浩,潘云鹤,庄越挺.基于对象形状的图像查询技术.软件学报,1998,9(5)：344349.
[99]Lebrun J, Foliguet S, Gosselin P. Image retrieval with graph kernel on regions. Proceedings of International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008:1-4.
[100]周明全,耿国华,韦娜.基于内容图像检索技术.北京：清华大学出版社,2007.
[101]陈允杰,张建伟,韦志辉,王平安.基于高斯混合模型的活动轮廓模型脑MRI分割.计算机研究与发展,2007,44(9)：1595-1603.
[102]李清勇,胡宏,施智平.基于纹理语义特征的图像检索研究.计算机学报,2006,29(1)：116-122.
[103]高中义.基于形状特征的图像分析算法研究：(硕士学位论文).长春：吉林大学,2002.
[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.计算机研究与发展,2003,40(11)：15661570.
[105]王文惠,周良柱,万建伟.基于内容的图像检索技术的研究和发展.计算机工程与应用,2001,37(5)：54-55.
[106]陈纯.计算机图像处理技术与算法.北京：清华大学出版社,2003.
[107]李弼程,彭天强,彭波.智能图像处理技术.北京：电子工业出版社,2004.
[108]孙兴华,郭丽,郭跃飞.基于目标区域的彩色图像检索研究.计算机研究与发展,2001,38(9)：11121119.
[109]张磊,林福宗,张钹.基于支持向量机的相关反馈图像检索算法.清华大学学报,2002,42(1)：8083.
[110]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机.北京：科学出版社,2004.
[111]李向阳,庄越挺,潘云鹤.基于内容的图像检索技术与系统.计算机研究与发展,2001,38(3)：344-354.
[112]李鹅杰,杨树元.一种基于内容的图像检索系统.微计算机应用,2001,22(3)：139-141.
[113]王亮申.图像特征提取及基于内容图像数据库检索理论和方法研究：(硕士学位论文).大连：大连理工大学,2002.
[114]张玲.基于内容的图像检索方法技术研究：(硕士学位论文).北京：北京化工大学,2005.
[115]张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1)：3242.
[116]Tsujinishi D, Abe S. Fuzzy least squares support vector machines for multi-class problems. Neural Networks,2003,16(5):785-792.
[117]付岩,王耀威,王伟强.SVM用于基于内容的自然图像分类和检索.计算机学报,2003,26(10)：1261—1265.
[118]Shi J, Malik J. Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[119]Derin H, Elliott H, Cristi R et al. Bayes smoothing algorithms for segmentation of binary images modeled by markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):707-720.
[120]Lim Y. W, Lee S. U. On the color image segmentation algorithm based on the thresholding and fuzzy c-means techniques. Pattern Recognition,1990,23(9):935-952.
[121]Prager J. M. Extracting and labeling boundary segments in natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence,1980,2(1):16-27.
[122]Inoue T, Abe S. Fuzzy support vector machines for pattern classification. In Proceedings of International.Joint Conference on Neural Networks, Washington,2001: 1449-1454.
[123]Lin C. F, Wang S. D. Fuzzy support vector machines. IEEE Transactions on Neural Networks, 2002,13(2):464-471.
[124]Efros A A, Freeman W T. Image quilting for texture synthesis and transfer. Proceedings of the 28th annual conference on Computer graphics and interactive techniques. ACM,2001: 341-346.
[125]Efros A A, Leung T K. Texture synthesis by non-parametric sampling. Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on. IEEE,1999,2: 1033-1038.
[126]汲业,陈燕,牟向伟,屈莉莉.基于内容的图像染色算法.Computer Engineering,2012,38(20).
[127]Kekre H B, Thepade S D. Color Traits Tranfer to Grayscale Images. Proceesings of the 1st International Conference on Emerging Trends in Engieering and Technology. Nagpur, Indua,2008:82-85.
[128]Pouli T, Reinharda E. Progressive Color Transfer for Images of Arbitary Dynamic. Computer&Grapgs,2011,35(1):67-80.
[129]Pitie F, Kokarama A C, Dahyota R. Automatid Color Grading using Colour Distribution Transfer. Computer Vision and Image Understanding.2007,107(1):123-137.
[130]宋麦玲,李欢.一种基于HSV颜色空间的图像检索技术.电脑知识与技术：学术交流,2007(1)：200-200.
[131]贾云涛,胡事民.基于图切分的交互式图像染色算法.计算机学报.2006,29(3)：508-512.
[132]Tomohisa W, Ashikhmin M. Transferring Color to Greyscale Images. Proceedings of ACM SIGGRAPH'02. San Antonio, USA; ACM Press,2002:227-280.
[133]陈路,李小东.基于BP神经网络的CMY到XYZ颜色空间转换算法研究.包装工程.2007,28(7),63-64.
[134]钟克洪,丁明跃,周成平,周亚安.基于均匀空间的颜色分级方法.中国图象图形学报.2004,9(11),1277-1281.
[135]龙永红,殷进.颜色空间变换及其在印刷中的应用.湖南工业大学学报.2007,21(006)：8-10.
[136]曹从军,刘强珺.基于径向基函数神经网络的颜色空间转换研究.中国印刷与包装研究.2010,2(11)：48-51.
[137]Reinhard E, Adhikhmin M, Gooch B, et al. Color transfer between images. Computer Graphics and Applications, IEEE,2001,21(5):34-41.
[138]李申燕.图像颜色迁移技术的研究.太原：中北大学.2009.
[139]黄伟,敬忠良,李建勋,等.基于1αβ空间的多光谱和全色图像融合.2006,32(11)：22-24.
[140]袁樵,詹庆旋.国际照明委员会照明标准——室内工作场所照明.照明工程学报.2002,13(4)：55-59.
[141]王浩,曾朝阳,兰永杰.LMS颜色空间稳定性在伪装评价中的应用.光电技术应用.200722(5)：10-12.
[142]Abdi. H., Williams, L. J. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics.2010,2:433-459.
[143]Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and intelligent laboratory systems.1987,2(1):37-52.
[144]陈小娥,陈昭炯.若干色彩传递应用模式的比较研究.计算机应用.2007,27(1)：146-147.
[145]D. L. Ruderman, T. W. Cronin, and C. C. Chiao. Statistics of coneresponses to natural images:implications for visual coding. J. Optical Soc. Of America.1998,15(8),2036-2045.
[146]韩静,罗立民,鲍旭东.基于小波变换的图像染色算法.中国图象图形学报,2007,12(9)：15791584.
[147]姚若河,黄继武.改进的直方图均衡化图象增强算法.铁道学报,1997,19(6)：7881.
[148]汪志云,黄梦为,胡钋,等.基于直方图的图像增强及其MATLAB实现.计算机工程与科学,2006,28(2)：5456.
[149]武治国,王延杰.一种基于直方图非线性变换的图像对比度增强方法.光子学报,2010,39(4)：755-758.
[150]施智平,胡宏,李清勇,等.一种快速有效的图像纹理谱描述子.计算机辅助设计与图形学学报,2004,16(012)：1703-1707.
[151]宋强,徐科,徐金梧.一类新的纹理谱描述子.计算机工程,2007,33(7)：30-32.
[152]柏子游,张勇.一种彩色图像的色彩分割方法.模式识别与人工智能,1999,12(002)：241-244.
[153]伯晓晨,刘建平.基于颜色直方图的图像检索.中国图象图形学报：A辑,1999,4(1)：3337.
[154]高美真,申艳梅.基于颜色直方图的图像检索技术.微电子学与计算机,2008,25(004)：25-27.
[155]Jain A K, Vailaya A. Image retrieval using color and shape. Pattern recognition,1996, 29(8):1233-1244.
© 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 |