数字图书馆图像检索技术研究与实现
摘要
目前对图像信息的管理技术可以说是基于像素的，而不是基于图像内容理解的。随着图像数据量的剧增，必须提供有效的图像分析和检索机制，使图像管理和检索高效、易行，这使得基于内容的图像研究成为必然。因为仅靠常用的文件标识、关键字或任何与图像相关的文本信息进行索引，局限性太大，无法进行直接基于视觉特性的检索。通过协调好人机分工，让计算机做它擅长的工作，由人来处理非结构化的问题，由此产生了基于内容的图像处理技术。它在不要求理解图像的前提下充分利用其内容的一些可计算特性，诸如颜色、纹理、形状等等，结合其它一些现有的成熟技术，来对图像信息进行存储、管理和检索。
本文首先在Dublin Core的基础上制定了适合我们要求的图像元数据集；详细分析了颜色、纹理、形状等视觉特征的提取和表示方法；探讨了图像视觉特征相似度量的问题，将模糊技术引入直方图的距离度量，分析了几何空间距离度量函数的不足之处，提出了系统中采用的距离函数；针对图像视觉特征向量的多维特性，分析了现有的各种降维技术和多维索引技术。最后综合以上技术，设计实现了一个综合的图像检索系统，集成了元数据检索、全文检索、基于视觉特征检索等多种检索方法，为用户提供友好的使用界面。
Now the management of image information is still based on pixels,but not on image's content. With the rapid increase of image data's amount,we need an efficient analysis and retrieve mechanism for image data to make the management and retrieve of image data efficiently and easily. All these requests make the study of content-based image processing necessary. We let the computer do what it can do,and let ourselves process the non-formalizable problem,we use calculable feature of image's content(color,texture,shape etc),also with some other mature technology to store,manage and retrieve image information.
In this paper,we first established the image metadata used in our system which based on the famous Dublin Core,then we analyzed the abstraction and description visual features of image such as color texture and shape. Next,we discussed the problem of similarity measure of visual feature,imported fuzzy logic into the distance feature and pointed out the disadvantages of geometry space based methods. For multi-dimension vector's high dimension nature,it's hard to index with traditional methods,we discussed how to lower the dimension using clustering and KLT transformation. We designed and implemented an image-retrieve system which combined available information retrieving technologies from metadata retrieve,text-retrieve and visual content based image retrieve fields. Also,our system had Web-browser based interface,especially for visual feature,user can visit our site easily and efficiently.
本文首先在Dublin Core的基础上制定了适合我们要求的图像元数据集；详细分析了颜色、纹理、形状等视觉特征的提取和表示方法；探讨了图像视觉特征相似度量的问题，将模糊技术引入直方图的距离度量，分析了几何空间距离度量函数的不足之处，提出了系统中采用的距离函数；针对图像视觉特征向量的多维特性，分析了现有的各种降维技术和多维索引技术。最后综合以上技术，设计实现了一个综合的图像检索系统，集成了元数据检索、全文检索、基于视觉特征检索等多种检索方法，为用户提供友好的使用界面。
Now the management of image information is still based on pixels,but not on image's content. With the rapid increase of image data's amount,we need an efficient analysis and retrieve mechanism for image data to make the management and retrieve of image data efficiently and easily. All these requests make the study of content-based image processing necessary. We let the computer do what it can do,and let ourselves process the non-formalizable problem,we use calculable feature of image's content(color,texture,shape etc),also with some other mature technology to store,manage and retrieve image information.
In this paper,we first established the image metadata used in our system which based on the famous Dublin Core,then we analyzed the abstraction and description visual features of image such as color texture and shape. Next,we discussed the problem of similarity measure of visual feature,imported fuzzy logic into the distance feature and pointed out the disadvantages of geometry space based methods. For multi-dimension vector's high dimension nature,it's hard to index with traditional methods,we discussed how to lower the dimension using clustering and KLT transformation. We designed and implemented an image-retrieve system which combined available information retrieving technologies from metadata retrieve,text-retrieve and visual content based image retrieve fields. Also,our system had Web-browser based interface,especially for visual feature,user can visit our site easily and efficiently.
引文
[1] Sclaroff S et al.ImageRover: a content-based image browser for the World-Wide Web.Proceedings of IEEE Workshop on Content-Based Access of Image and Video Libraries .San Juan, Puerto Rico, June 1997, 2-9
[2] Gudivada V N and Raghavan V V. Design and evaluation of algorithms for image retrieval by spatial similarity. 1995,ACM Transactions on Information Systems 13(2) , 115-144
[3] Jain, R .World-wide maze. 1995,IEEE Multimedia 2(3) , 3
[4] Eakins J P, Graham M E and Boardman J M. Evaluation of a trademark retrieval syste, in 19th BCS IRSG Research Colloquium on Information Retrieval.Robert Gordon University, Aberdeen, electronic Workshops in Computing, Springer-Verlag, Berlin .1997
[5] Flickner, M et al. Query by image and video content: the QBIC system. 1995, IEEE Computer 28(9) , 23-32
[6] J. R. Smith and S-F. Chang. VisualSEEk: a fully automated content-based image query system. ACM Multimedia '96． November, 1996
[7] Shi-Kuo Chang and King-Sun Fu. Picture Query Languages for Pictorial Data-Base Ssystems. IEEE Computer, 14(11) :23-42, November 1981
[8] RameshJain.Infoscopes:information Systems for the Next Century.Proceedings of Multimedia Information System and Hypermedia. Tokyo, Japan,March,1995
[9] Pentland,R.W.Picard,S.Sclaroff. Photobook:Tools for content-based manipulation of image databases.SPIE Symposium on Electronic Imaging Science and Technology: Storage and Retrieval for Image Video Databases,V2185, San Jose. CA.1994
[10] Virginia E. Ogle, Michael Stonebraker. Chabot: Retrieval from a Relational Database of Images. IEEE computer, 1995
[11] G.Salton and M.J.McGill.Introduction to Modern Information Retrieval. McGraw-Hill Book Company. 1983
[12] Tat-Seng Chua, Swee-Kiew Lim and Hung-Keng Pung.Content-based Retrieval of segmented Images, in ACM Multimedia'94, 1994
[13] John R.Smith and Shih-fu Chang.Quad-Tree Segmentation For Texture-based Image Query, in ACM Multimedia'94． 1994
[14] Rohini K.Srihari. Automatic Indexing and Content-Based Retrieval of Captionaed Images, IEEE Computer pp49-56,1995
[15] Weibel, Stuart. 1999． Dublin Core and the Metadata Landscape: Conventions for Semantics, Syntax, and Structure in the Internet Commons.
[16] Beckett, D. Redland RDF Application Framework. http://www.redland. opensource.ac.uk/. 2001
[17] Visual Resources Association Standards Committee. VRA core categories, version 3． 0． Technical report, Visual Resources Association, July 2000． http://www.gsd.harvard.edu/ staffaw3/vra/vracore3 .htm
[18] R. Picard, T. Kabir, and F. Liu. Real-time Recognition with the Entire Brodatz Texture Database. IEEE Conf. on Comp. Vis. and Pattern Recognition, pages 638--639, New York 1993
[19]R.M. Haralick. Texture features for image classification. IEEE Trans. Syst, Man and Cybern., SMC-3(6):610--621, 1973
[20]C. Dorai and A.K. Jain. Shape spectrum based view grouping and matching of 3d free-form objects. IEEE PAMI, 19(10): 1139--1146, 1997
[21]V. Gudivada and V. Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Transactions on Information Systems, 13(2):115--144, April 1995.
[22]Yong Rui, Alfred C. She, and Thomas S. Huang. Modied fourier descriptors for shape representation:a practical approach. In Proc. of First International Workshop on Image Databases and Multi Media Search, 1996
[23]Hu, M.K.Visual pattern recognition by moment invariants. IEEE Trans. Inform. Theory, 8:179-187, 1962
[24]Fred Attneave. Dimensions of similarity. Americal Journal of Psychology, 63:516--556, 1950
[25]Tversky, Amos (1977). Features of Similarity. Psychological Review, 84-4, pp. 327-352.
[26]Jacopo M.Corridoni, Alberto Del Bimbo, Silvio Demagistris. Querying and Retrieving Pictorial Data using Semantics Induced by colour Quality and Arrangement. IEEE Proceedings of Multimedia'96. 1996.
[27]D.Zhang and S.F. Chang. Video Object Model and Segmentation for Content-Based Video Indexing,IEEE International Conference on Circuits and Systems. Hong Kong. 1997
[28]H.S.Sawheny, S.Ayer, M.Gorkani.Model-based 2D&3D Dominant Motion Eastimation for Mosaicing and represention,International conference on Computer Vision, 583-590, Boston. Jun,1995
[29]E.M.Arkin, L.P. Chew. D.p. Huttenlocher, K.Kadem,J.S.b. Mitchell, An Efficiently Computable Metric for Comparing Polygonal Shapes IEEE Trans.on PAMI, Vol.13,209-216,Mar, 1991.
[30]Y. Rui,T.S.Huang,and S.Mehrotra. Exploring video structions beyond the shots"in Proc. of IEEE conf. Multimedia Computing and Systems, 1998.
[31]Tat-Seng Chua, Swee-Kiew Lim and Hung-Keng Pung. Content-based Retrieval of segmented Images. ACM Multimedia'94, 1994.
[32]G. Salton and M.J.McGill. Introduction to Modern Information Retrieval. McGraw-Hill Book Company. 1983.
[33]Bierling. Displacement estimation by Hierarical block Matching. SPIE Visual Communications and Image Processing,vol.1001,1998
[34]Zhong D, Zhang H J, Chang S-F. Clustering methods for video browsing and annotation. Proceedings Of SPIE Conference on storage and retrieval for image and video database: IV, 1996.239-246.1996
[35]高文，刘峰，黄铁军等著．数字图书馆——原理与技术实现。北京：清华大学出版社，2000
[36]沈清，汤霖．模式识别导论．长沙：国防科大出版社．1990．8．
[37]李国辉，王辰，薛峰．几种典型的基于内容检索系统．计算机世界报技术专题，1998
[38]李国辉，胡晓峰．基于内容的检索．计算机世界报技术专题．1998
[39]鲁东明，潘云鹤，张扬．基于色彩和线描特征相似度计算的敦煌壁画检索．智能计算机接口与应用进展—第三届中国计算机智能接口与智能应用学术会议论文
集．1997．
[40] 白雪生，徐光佑，史元春．镜头频度用于视频检索的研究．软件学报，1999
[41] 曹丽华．基于内容检索中的视频处理技术研究．计算机工程与应用 1998，6
[42] 曹莉华．视频媒体的基于内容处理和检索的研究与实现：[博士学位论文]．长沙：国防科学技术大学七系，1998．
[43] 薛峰．基于内容检索的图像和视频存储结构和索引技术的研究和实现：[硕士学位论文]，长沙：国防科学技术大学七系，1999．
[44] 柳伟．基于内容的图像检索技术研究和实现：[硕士学位论文]．长沙：国防科学技术大学七系，1999．
[2] Gudivada V N and Raghavan V V. Design and evaluation of algorithms for image retrieval by spatial similarity. 1995,ACM Transactions on Information Systems 13(2) , 115-144
[3] Jain, R .World-wide maze. 1995,IEEE Multimedia 2(3) , 3
[4] Eakins J P, Graham M E and Boardman J M. Evaluation of a trademark retrieval syste, in 19th BCS IRSG Research Colloquium on Information Retrieval.Robert Gordon University, Aberdeen, electronic Workshops in Computing, Springer-Verlag, Berlin .1997
[5] Flickner, M et al. Query by image and video content: the QBIC system. 1995, IEEE Computer 28(9) , 23-32
[6] J. R. Smith and S-F. Chang. VisualSEEk: a fully automated content-based image query system. ACM Multimedia '96． November, 1996
[7] Shi-Kuo Chang and King-Sun Fu. Picture Query Languages for Pictorial Data-Base Ssystems. IEEE Computer, 14(11) :23-42, November 1981
[8] RameshJain.Infoscopes:information Systems for the Next Century.Proceedings of Multimedia Information System and Hypermedia. Tokyo, Japan,March,1995
[9] Pentland,R.W.Picard,S.Sclaroff. Photobook:Tools for content-based manipulation of image databases.SPIE Symposium on Electronic Imaging Science and Technology: Storage and Retrieval for Image Video Databases,V2185, San Jose. CA.1994
[10] Virginia E. Ogle, Michael Stonebraker. Chabot: Retrieval from a Relational Database of Images. IEEE computer, 1995
[11] G.Salton and M.J.McGill.Introduction to Modern Information Retrieval. McGraw-Hill Book Company. 1983
[12] Tat-Seng Chua, Swee-Kiew Lim and Hung-Keng Pung.Content-based Retrieval of segmented Images, in ACM Multimedia'94, 1994
[13] John R.Smith and Shih-fu Chang.Quad-Tree Segmentation For Texture-based Image Query, in ACM Multimedia'94． 1994
[14] Rohini K.Srihari. Automatic Indexing and Content-Based Retrieval of Captionaed Images, IEEE Computer pp49-56,1995
[15] Weibel, Stuart. 1999． Dublin Core and the Metadata Landscape: Conventions for Semantics, Syntax, and Structure in the Internet Commons.
[16] Beckett, D. Redland RDF Application Framework. http://www.redland. opensource.ac.uk/. 2001
[17] Visual Resources Association Standards Committee. VRA core categories, version 3． 0． Technical report, Visual Resources Association, July 2000． http://www.gsd.harvard.edu/ staffaw3/vra/vracore3 .htm
[18] R. Picard, T. Kabir, and F. Liu. Real-time Recognition with the Entire Brodatz Texture Database. IEEE Conf. on Comp. Vis. and Pattern Recognition, pages 638--639, New York 1993
[19]R.M. Haralick. Texture features for image classification. IEEE Trans. Syst, Man and Cybern., SMC-3(6):610--621, 1973
[20]C. Dorai and A.K. Jain. Shape spectrum based view grouping and matching of 3d free-form objects. IEEE PAMI, 19(10): 1139--1146, 1997
[21]V. Gudivada and V. Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Transactions on Information Systems, 13(2):115--144, April 1995.
[22]Yong Rui, Alfred C. She, and Thomas S. Huang. Modied fourier descriptors for shape representation:a practical approach. In Proc. of First International Workshop on Image Databases and Multi Media Search, 1996
[23]Hu, M.K.Visual pattern recognition by moment invariants. IEEE Trans. Inform. Theory, 8:179-187, 1962
[24]Fred Attneave. Dimensions of similarity. Americal Journal of Psychology, 63:516--556, 1950
[25]Tversky, Amos (1977). Features of Similarity. Psychological Review, 84-4, pp. 327-352.
[26]Jacopo M.Corridoni, Alberto Del Bimbo, Silvio Demagistris. Querying and Retrieving Pictorial Data using Semantics Induced by colour Quality and Arrangement. IEEE Proceedings of Multimedia'96. 1996.
[27]D.Zhang and S.F. Chang. Video Object Model and Segmentation for Content-Based Video Indexing,IEEE International Conference on Circuits and Systems. Hong Kong. 1997
[28]H.S.Sawheny, S.Ayer, M.Gorkani.Model-based 2D&3D Dominant Motion Eastimation for Mosaicing and represention,International conference on Computer Vision, 583-590, Boston. Jun,1995
[29]E.M.Arkin, L.P. Chew. D.p. Huttenlocher, K.Kadem,J.S.b. Mitchell, An Efficiently Computable Metric for Comparing Polygonal Shapes IEEE Trans.on PAMI, Vol.13,209-216,Mar, 1991.
[30]Y. Rui,T.S.Huang,and S.Mehrotra. Exploring video structions beyond the shots"in Proc. of IEEE conf. Multimedia Computing and Systems, 1998.
[31]Tat-Seng Chua, Swee-Kiew Lim and Hung-Keng Pung. Content-based Retrieval of segmented Images. ACM Multimedia'94, 1994.
[32]G. Salton and M.J.McGill. Introduction to Modern Information Retrieval. McGraw-Hill Book Company. 1983.
[33]Bierling. Displacement estimation by Hierarical block Matching. SPIE Visual Communications and Image Processing,vol.1001,1998
[34]Zhong D, Zhang H J, Chang S-F. Clustering methods for video browsing and annotation. Proceedings Of SPIE Conference on storage and retrieval for image and video database: IV, 1996.239-246.1996
[35]高文，刘峰，黄铁军等著．数字图书馆——原理与技术实现。北京：清华大学出版社，2000
[36]沈清，汤霖．模式识别导论．长沙：国防科大出版社．1990．8．
[37]李国辉，王辰，薛峰．几种典型的基于内容检索系统．计算机世界报技术专题，1998
[38]李国辉，胡晓峰．基于内容的检索．计算机世界报技术专题．1998
[39]鲁东明，潘云鹤，张扬．基于色彩和线描特征相似度计算的敦煌壁画检索．智能计算机接口与应用进展—第三届中国计算机智能接口与智能应用学术会议论文
集．1997．
[40] 白雪生，徐光佑，史元春．镜头频度用于视频检索的研究．软件学报，1999
[41] 曹丽华．基于内容检索中的视频处理技术研究．计算机工程与应用 1998，6
[42] 曹莉华．视频媒体的基于内容处理和检索的研究与实现：[博士学位论文]．长沙：国防科学技术大学七系，1998．
[43] 薛峰．基于内容检索的图像和视频存储结构和索引技术的研究和实现：[硕士学位论文]，长沙：国防科学技术大学七系，1999．
[44] 柳伟．基于内容的图像检索技术研究和实现：[硕士学位论文]．长沙：国防科学技术大学七系，1999．