基于纹理特征图像检索的研究与实现
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摘要
随着计算机网络和多媒体技术的发展,越来越多的图像信息出现在人们的生活中,那么如何在海量图像数据中找出所需要的图像成为研究热点。基于内容的图像检索技术应运而生,它不同于传统基于文本的检索,实际上是一种模糊查询技术,通过对图像提取一定的特征,找出在特征空间中与查询要求接近的图像。
本文主要对基于纹理的图像检索技术进行了深入研究。首先对基于内容图像检索技术的基本原理和框架进行了概要介绍。然后对纹理特征的提取进行了深入的研究,重点研究的是灰度共生矩阵方法和包含颜色信息的三维共生矩阵方法,之后研究了基于内容图像检索的关键技术,包括相似性度量和相关反馈,最后设计并实现了一个基于纹理的图像检索系统,对系统的性能进行了评价,并提出了下一步的研究方向。
在系统的实现部分,本文采用了一种边计算边排序的方法进行结果图像的相似程度排序。并为系统添加了相关反馈机制,通过调整权值进行重新检索后使用新的规则进行排序显示。通过对Corel图像库进行实验的结果表明,将三维共生矩阵法和本文设计的相关反馈机制相结合,能够使系统检索结果更符合用户需求。
According to the development of computer network and multimedia technology, more and more image information appear in people's lives. How to find out the picture we need in huge amounts of image data became a research focus. Therefore content-based image retrieval technology appeared. It is different from traditional text-based search. It is actually a fuzzy inquiry. By extracting certain features of the image it finds out similar images in feature space.
The texture-based image retrieval technology is deeply studied in this paper. Firstly, the basic theory and frame of content-based image retrieval technology are introduced. Then the extraction of texture feature is studied deeply, focusing on the research of Gray Level Co-occurrence Matrix and the 3D Co-occurrence Matrix which including color information. Afterwards, the research of key technologies on content-based image retrieval system is done, including similarity measure and relevance feedback. Finally, designs and implements a texture-based image retrieval system and evaluates the system's performance and proposes future research directions.
In the part of system implementation, the paper used a method to sort result images based on similarity as calculating. And a relevance feedback mechanism is added to the system, by adjusting the weight to search again then sort and show images using the new rules. Through the experiments of Corel image library, the results showed that by combine 3D Co-occurrence Matrix method and the relevance feedback mechanism designed in this paper can make the search results more meet users' needs.
本文主要对基于纹理的图像检索技术进行了深入研究。首先对基于内容图像检索技术的基本原理和框架进行了概要介绍。然后对纹理特征的提取进行了深入的研究,重点研究的是灰度共生矩阵方法和包含颜色信息的三维共生矩阵方法,之后研究了基于内容图像检索的关键技术,包括相似性度量和相关反馈,最后设计并实现了一个基于纹理的图像检索系统,对系统的性能进行了评价,并提出了下一步的研究方向。
在系统的实现部分,本文采用了一种边计算边排序的方法进行结果图像的相似程度排序。并为系统添加了相关反馈机制,通过调整权值进行重新检索后使用新的规则进行排序显示。通过对Corel图像库进行实验的结果表明,将三维共生矩阵法和本文设计的相关反馈机制相结合,能够使系统检索结果更符合用户需求。
According to the development of computer network and multimedia technology, more and more image information appear in people's lives. How to find out the picture we need in huge amounts of image data became a research focus. Therefore content-based image retrieval technology appeared. It is different from traditional text-based search. It is actually a fuzzy inquiry. By extracting certain features of the image it finds out similar images in feature space.
The texture-based image retrieval technology is deeply studied in this paper. Firstly, the basic theory and frame of content-based image retrieval technology are introduced. Then the extraction of texture feature is studied deeply, focusing on the research of Gray Level Co-occurrence Matrix and the 3D Co-occurrence Matrix which including color information. Afterwards, the research of key technologies on content-based image retrieval system is done, including similarity measure and relevance feedback. Finally, designs and implements a texture-based image retrieval system and evaluates the system's performance and proposes future research directions.
In the part of system implementation, the paper used a method to sort result images based on similarity as calculating. And a relevance feedback mechanism is added to the system, by adjusting the weight to search again then sort and show images using the new rules. Through the experiments of Corel image library, the results showed that by combine 3D Co-occurrence Matrix method and the relevance feedback mechanism designed in this paper can make the search results more meet users' needs.
引文
1.董亮.一种基于内容的图像检索方法[J].科技情报开发与经济,2007,17(15)：245-247.
2.韩存兵Visual C++数据库编程实战[M].北京：科学出版社,2003.
3.范自柱.基于内容的图像检索技术综述[J].华东交通大学学报,2005,22(5)：147-150.
4.黄元元,何云峰.利用颜色进行基于内容的图像检索[J].小型微型计算机系统,2007,7(7)：1277-1281.
5.刘倩.基于内容的图像检索中的相关反馈技术[J].华东交通大学学报,2003,20(4)：71-74.
6.刘忠伟,章毓晋.综合利用颜色和纹理特征的图像检索[J].通信学报,1999,20(5)：36-40.
7.李学龙,刘政凯,俞能海.一种有效的基于内容的图像检索方法[J].通信学报,2002,23(12)：82-85.
8.李迎新.基于颜色和纹理特征的图像检索研究[D].上海：上海海事大学,2007.
9.沈炜,徐慧Visual C++数据库编程技术与实例[M].北京：人民邮电出版社,2005.
10.盛文,杨江平,柳建,等.一种基于纹理元灰度模式统计的图像纹理分析方法[J].电子学报,2000,28(4)：1-4.
11.苏彦华Visual C++数字图像识别技术典型案例[M].北京：人民邮电出版社,2004.
12.王波,姚宏宇,李弼程.一种有效的基于灰度共生矩阵的图像检索方法[J].武汉大学学报,2006,31(9)：761-764.
13.王海霞.基于纹理特征的图像检索技术研究[D].秦皇岛：燕山大学,2006.
14.韦娜,耿国华,周明全.基于内容的图像检索系统性能评价[J].中国图象图形学报,2004,9(11)：1271-1276.
15.王水璋.基于纹理的图像检索技术研究[D].太原：太原理工大学,2008.
16.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)：1970-1977.
17.许录平.数字图像处理[M].北京：科学出版社,2007.
18.徐庆,杨维维,陈生潭.基于内容的图像检索技术[J].计算机技术与发展,2008,18(1)：126-131.
19.谢毓湘,吴玲达,栾悉道.基于内容的图像检索技术研究[J].计算机工程与应用,2002,1：35-38.
20.杨波,徐光祐.纹理相似性度量研究及基于纹理特征的图像检索[J].自动化学报,2004,30(6)：991-998.
21.张水利,任淑萍,王欣峰.基于内容的图像检索技术的现状和发展趋势研究[J].科技情报开发与经济,2007,17(17)：183-184.
22.赵荣椿等.数字图象处理导论[M].西安：西北工业大学出版社,1995.
23.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.
24.周文昭,夏定员,周曼丽,许毅平.基于内容图像检索系统的最新进展[J].计算机工程与应用,2003,26：112-124.
25.庄越挺,潘云鹤,吴飞编.网上多媒体信息分析与检索[M].北京：清华大学出版社,2002.
26.A.Baraldi, P.Blonda. Multi-scale Texture Feature Representation for Content-based Image Database Retrieval[J]. European Space Agency (Special Publication),2004, (553):13-16.
27.Amarnath Gupta, Ramesh Jain. Visual Information Retrieval [J].Communications of the ACM, 1997,40(5):71-79.
28.A.Pentland, R.W.Picard, Sclaroff. Photobook:Content-based Manipulation of Image Databases[J]. International Journal of Computer Vision,1996,18(3):233-254.
29.A.Rappoport. Search Engines:the Hunt is on[J].Network Computing,2000,11(20):48-70.
30.Dorin Comaniciu, Peter Meer, David Tyler. Dissimilarity computation through low rank corrections[J].Pattern Recognition Letters,2003,24:227-236.
31.G.L.Gimelfarb, A.K.Jain. On Retrieval Texture Images from an Image Databases [J].Pattern Recognition,1996,29(9):1461-1483.
32.H.Long, C.W.Tan, W.K.Leow. Invariant and Perceptually Consistent Texture Mapp-ing for Content-based Image Retrieval [C]. IEEE International Conference on Image Processing, Salonicco,Greece,2001:117-120.
33.H.Tamura, S.Mori, T.Yamawaki. Texture Features Corresponding to Visual Perception[J].IEEE Transactions on Systems,Man and Cybernetics,1978,8(6):460-473.
34.Hsu Wynne, T.S.Chua, H.K.Pung. Approximating Content-based Object-level ImageRetrieval[J]. Multimedia Tools and Applications,2000,12(1):59-79.
35.Hua Xie, Antonio Omega. Feature Representation and Compression for Content-based Retrieval[J]. Proceedings of SPIE-The International Society for Optical Engineering,2001,4310:111-122.
36.J.R.Smith, S.F.Chang. Visually Searching the Web for Content[J].IEEE Multimedia Magazine, 1997,4(3):12-20.
37.Kokare Manesh, B.N.Chatterji, P.K.Biswas. Cosine-modulated Wavelet Based Texture Features for Content-based Image Retrieval[J].Pattern Recognition Letters,2004,25(4):391-398.
38.LI Xuelong, LIU Zhengkai, YU Nenghai, et al. Generalized 3D Co-occurrence Matrix for Visual Information Retrieval[J]. Journal of circuits and systems,2002,7(2):10-12.
39.LuzMarina Quiroga, Javed Mostafa. An Experiment in Building Profiles in the Infor-mation Filtering:the Role of Context of User Relevance Feedback[J].Processing and Management,2001, 38(5):671-694.
40.M.Fliekner, H.Sawhney, W.niblaek, et al. Query by Image and Video Content:the QBIC System[J]. IEEE Computer,1995,28(9):23-32.
41.O.E.Badawy, M.Kamel. Shape-based Image Retrieval Applied to Trademark Images[J]. International Journal of Image and Graphics,1997,2(3):375-394.
42.P.W.Huang, S.K.Dai. Design of a Two-stage Content-based Image Retrieval System Using Texture Similarity [J]. Information Processing and Management,2004,40(1):81-96.
43.Payne Janet S, Stonham John. Mapping Perceptual Texture Similarity for Image Retrieval[J]. Lecture Notes in Computer Science,2005,4540:960-969.
44.R.M.Haralick. Statistical and Structural Approaches to Texture[J].Proceeding of IEEE,1979,67 (5):786-804.
45.Tao Bo, B.W.Dickinson. Texture Recognition and Image Retrieval Using Gradient Indexing[J]. Journal of Visual Communication and Image Representation,2000,11(3):327-342.
46.Vittorio Castelli. Multidimensional Indexing Structures Content-based Retrieval[R].IBM Research Report, RC 22208(98723) February 13,2001.
47.W.Y.Ma, B.S. Manjunath.Netra:a Toolbox for Navigating Large Image Databases[J]. Multimedia Systems,1999,7(3):98-184.
48.Y.D.Chun, J.K.Sung, N.C.Kim. Image Retrieval Using Combination of Color and Multiresolution Texture Features[J]. Proceedings of SPIE-The International Society for Optical Engineering,2005, 5682:195-203.
49.Zheng Lei, A.W.Wetzel, Gilbertson John, et al. Design and Analysis of a Content-Based Pathology Image Retrieval System [J]. IEEE Transactions on Information Techno-logy in Biomedicine,2003, 7(4):249-255.
2.韩存兵Visual C++数据库编程实战[M].北京：科学出版社,2003.
3.范自柱.基于内容的图像检索技术综述[J].华东交通大学学报,2005,22(5)：147-150.
4.黄元元,何云峰.利用颜色进行基于内容的图像检索[J].小型微型计算机系统,2007,7(7)：1277-1281.
5.刘倩.基于内容的图像检索中的相关反馈技术[J].华东交通大学学报,2003,20(4)：71-74.
6.刘忠伟,章毓晋.综合利用颜色和纹理特征的图像检索[J].通信学报,1999,20(5)：36-40.
7.李学龙,刘政凯,俞能海.一种有效的基于内容的图像检索方法[J].通信学报,2002,23(12)：82-85.
8.李迎新.基于颜色和纹理特征的图像检索研究[D].上海：上海海事大学,2007.
9.沈炜,徐慧Visual C++数据库编程技术与实例[M].北京：人民邮电出版社,2005.
10.盛文,杨江平,柳建,等.一种基于纹理元灰度模式统计的图像纹理分析方法[J].电子学报,2000,28(4)：1-4.
11.苏彦华Visual C++数字图像识别技术典型案例[M].北京：人民邮电出版社,2004.
12.王波,姚宏宇,李弼程.一种有效的基于灰度共生矩阵的图像检索方法[J].武汉大学学报,2006,31(9)：761-764.
13.王海霞.基于纹理特征的图像检索技术研究[D].秦皇岛：燕山大学,2006.
14.韦娜,耿国华,周明全.基于内容的图像检索系统性能评价[J].中国图象图形学报,2004,9(11)：1271-1276.
15.王水璋.基于纹理的图像检索技术研究[D].太原：太原理工大学,2008.
16.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)：1970-1977.
17.许录平.数字图像处理[M].北京：科学出版社,2007.
18.徐庆,杨维维,陈生潭.基于内容的图像检索技术[J].计算机技术与发展,2008,18(1)：126-131.
19.谢毓湘,吴玲达,栾悉道.基于内容的图像检索技术研究[J].计算机工程与应用,2002,1：35-38.
20.杨波,徐光祐.纹理相似性度量研究及基于纹理特征的图像检索[J].自动化学报,2004,30(6)：991-998.
21.张水利,任淑萍,王欣峰.基于内容的图像检索技术的现状和发展趋势研究[J].科技情报开发与经济,2007,17(17)：183-184.
22.赵荣椿等.数字图象处理导论[M].西安：西北工业大学出版社,1995.
23.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.
24.周文昭,夏定员,周曼丽,许毅平.基于内容图像检索系统的最新进展[J].计算机工程与应用,2003,26：112-124.
25.庄越挺,潘云鹤,吴飞编.网上多媒体信息分析与检索[M].北京：清华大学出版社,2002.
26.A.Baraldi, P.Blonda. Multi-scale Texture Feature Representation for Content-based Image Database Retrieval[J]. European Space Agency (Special Publication),2004, (553):13-16.
27.Amarnath Gupta, Ramesh Jain. Visual Information Retrieval [J].Communications of the ACM, 1997,40(5):71-79.
28.A.Pentland, R.W.Picard, Sclaroff. Photobook:Content-based Manipulation of Image Databases[J]. International Journal of Computer Vision,1996,18(3):233-254.
29.A.Rappoport. Search Engines:the Hunt is on[J].Network Computing,2000,11(20):48-70.
30.Dorin Comaniciu, Peter Meer, David Tyler. Dissimilarity computation through low rank corrections[J].Pattern Recognition Letters,2003,24:227-236.
31.G.L.Gimelfarb, A.K.Jain. On Retrieval Texture Images from an Image Databases [J].Pattern Recognition,1996,29(9):1461-1483.
32.H.Long, C.W.Tan, W.K.Leow. Invariant and Perceptually Consistent Texture Mapp-ing for Content-based Image Retrieval [C]. IEEE International Conference on Image Processing, Salonicco,Greece,2001:117-120.
33.H.Tamura, S.Mori, T.Yamawaki. Texture Features Corresponding to Visual Perception[J].IEEE Transactions on Systems,Man and Cybernetics,1978,8(6):460-473.
34.Hsu Wynne, T.S.Chua, H.K.Pung. Approximating Content-based Object-level ImageRetrieval[J]. Multimedia Tools and Applications,2000,12(1):59-79.
35.Hua Xie, Antonio Omega. Feature Representation and Compression for Content-based Retrieval[J]. Proceedings of SPIE-The International Society for Optical Engineering,2001,4310:111-122.
36.J.R.Smith, S.F.Chang. Visually Searching the Web for Content[J].IEEE Multimedia Magazine, 1997,4(3):12-20.
37.Kokare Manesh, B.N.Chatterji, P.K.Biswas. Cosine-modulated Wavelet Based Texture Features for Content-based Image Retrieval[J].Pattern Recognition Letters,2004,25(4):391-398.
38.LI Xuelong, LIU Zhengkai, YU Nenghai, et al. Generalized 3D Co-occurrence Matrix for Visual Information Retrieval[J]. Journal of circuits and systems,2002,7(2):10-12.
39.LuzMarina Quiroga, Javed Mostafa. An Experiment in Building Profiles in the Infor-mation Filtering:the Role of Context of User Relevance Feedback[J].Processing and Management,2001, 38(5):671-694.
40.M.Fliekner, H.Sawhney, W.niblaek, et al. Query by Image and Video Content:the QBIC System[J]. IEEE Computer,1995,28(9):23-32.
41.O.E.Badawy, M.Kamel. Shape-based Image Retrieval Applied to Trademark Images[J]. International Journal of Image and Graphics,1997,2(3):375-394.
42.P.W.Huang, S.K.Dai. Design of a Two-stage Content-based Image Retrieval System Using Texture Similarity [J]. Information Processing and Management,2004,40(1):81-96.
43.Payne Janet S, Stonham John. Mapping Perceptual Texture Similarity for Image Retrieval[J]. Lecture Notes in Computer Science,2005,4540:960-969.
44.R.M.Haralick. Statistical and Structural Approaches to Texture[J].Proceeding of IEEE,1979,67 (5):786-804.
45.Tao Bo, B.W.Dickinson. Texture Recognition and Image Retrieval Using Gradient Indexing[J]. Journal of Visual Communication and Image Representation,2000,11(3):327-342.
46.Vittorio Castelli. Multidimensional Indexing Structures Content-based Retrieval[R].IBM Research Report, RC 22208(98723) February 13,2001.
47.W.Y.Ma, B.S. Manjunath.Netra:a Toolbox for Navigating Large Image Databases[J]. Multimedia Systems,1999,7(3):98-184.
48.Y.D.Chun, J.K.Sung, N.C.Kim. Image Retrieval Using Combination of Color and Multiresolution Texture Features[J]. Proceedings of SPIE-The International Society for Optical Engineering,2005, 5682:195-203.
49.Zheng Lei, A.W.Wetzel, Gilbertson John, et al. Design and Analysis of a Content-Based Pathology Image Retrieval System [J]. IEEE Transactions on Information Techno-logy in Biomedicine,2003, 7(4):249-255.