基于块划分颜色特征的图像检索方法
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摘要
本文提出了一种新的基于内容检索图像的方法——基于块划分颜色特征的图像检索方法。该方法利用栅格划分技术提取图像颜色特征，通过对图像的分块编码，将图像转换成类似文本的形式，并借用成熟的文本模型分析图像特征分布。
由于利用颜色特征能够简化目标的提取和识别，所以在图像检索中，颜色是应用最广泛的视觉属性。传统的表示颜色特征的直方图法只能表示颜色的组成，没有包含颜色的空间分布信息，难以区分颜色组成相似但是空间分布不同的图像。为了克服颜色直方图的缺点，本文提出了一种新的基于颜色及其空间分布的图像检索方法。该方法将图像划分成大小相等的图像块，然后提取每一块的颜色信息作为特征矢量。通过对特征矢量聚类编码，图像内容可以表示为空间分布信息的局部颜色特征组合，进而可以应用基于文本的检索技术实现图像检索。实验证明，用此方法表示图像不但可以实现对无约束场景图像的有效检索，而且能够较好地实现查询和定位区域图像。
在所有基于块分割的图像描述方法中，块大小的选择是一个影响特征表示有效性的重要问题，以往的方法中都依赖于经验选择分割尺度。本文着重对该问题进行了研究，采用信息熵作为衡量划分优劣的标准，在优化的意义下对块划分窗口进行选择。实验证明，这样选择的划分窗口是有意义的，能够提高检索的有效性。
This paper provides a new image retrieval approach: Partition-based color image retrieval. Partition is used to extract spatial information and by coding, image can be transformed into a text-image. So images can be analyzed by mature text model.
Color is a common used feature in content-based image retrieval for it simplifies object identification. The traditional approach to using the color information of an image is color histogram, which is insensitive to translation and rotation. But its drawback is prone to yield false hits in large database because of lacking spatial information. To solute the problem, we try to combine spatial information with color feature to improve the performance of content-based image retrieval. This is achieved by partitioning images in the training set into fixed size cells and, for each cell, extracting a local color histogram as the color invariant feature of the cell. All of the color invariant features are clustered into a number of patterns. Thus all the images in the database can be regarded as a collection of those patterns. Images are recognized by their patterns, which takes a step to retrieve image by symbolic notions. Thereby the well-developed text retrieval method can be applied for image'query and index t
hrough such symbolic descriptions. Experimental results show that the new method is robust in retrieving images with domain-free scenes and is efficient in sub-region retrieval and localization.
For all the partition-based image retrieval, choosing a proper partitioning scale is a significant problem and is usually determined subjectively. This paper proposes information entropy as a measure of optimal scale and experimental results show it is reasonable.
由于利用颜色特征能够简化目标的提取和识别，所以在图像检索中，颜色是应用最广泛的视觉属性。传统的表示颜色特征的直方图法只能表示颜色的组成，没有包含颜色的空间分布信息，难以区分颜色组成相似但是空间分布不同的图像。为了克服颜色直方图的缺点，本文提出了一种新的基于颜色及其空间分布的图像检索方法。该方法将图像划分成大小相等的图像块，然后提取每一块的颜色信息作为特征矢量。通过对特征矢量聚类编码，图像内容可以表示为空间分布信息的局部颜色特征组合，进而可以应用基于文本的检索技术实现图像检索。实验证明，用此方法表示图像不但可以实现对无约束场景图像的有效检索，而且能够较好地实现查询和定位区域图像。
在所有基于块分割的图像描述方法中，块大小的选择是一个影响特征表示有效性的重要问题，以往的方法中都依赖于经验选择分割尺度。本文着重对该问题进行了研究，采用信息熵作为衡量划分优劣的标准，在优化的意义下对块划分窗口进行选择。实验证明，这样选择的划分窗口是有意义的，能够提高检索的有效性。
This paper provides a new image retrieval approach: Partition-based color image retrieval. Partition is used to extract spatial information and by coding, image can be transformed into a text-image. So images can be analyzed by mature text model.
Color is a common used feature in content-based image retrieval for it simplifies object identification. The traditional approach to using the color information of an image is color histogram, which is insensitive to translation and rotation. But its drawback is prone to yield false hits in large database because of lacking spatial information. To solute the problem, we try to combine spatial information with color feature to improve the performance of content-based image retrieval. This is achieved by partitioning images in the training set into fixed size cells and, for each cell, extracting a local color histogram as the color invariant feature of the cell. All of the color invariant features are clustered into a number of patterns. Thus all the images in the database can be regarded as a collection of those patterns. Images are recognized by their patterns, which takes a step to retrieve image by symbolic notions. Thereby the well-developed text retrieval method can be applied for image'query and index t
hrough such symbolic descriptions. Experimental results show that the new method is robust in retrieving images with domain-free scenes and is efficient in sub-region retrieval and localization.
For all the partition-based image retrieval, choosing a proper partitioning scale is a significant problem and is usually determined subjectively. This paper proposes information entropy as a measure of optimal scale and experimental results show it is reasonable.
引文
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[2] John Zachary, S. S. Iyengar, Jacob Barhen. Content based image retrieval and information theory: a general approach. Journal of the American Society for Information Science and Technology, 2001, 52(10): 840-852
[3] N. S. Chang, K. S. Fu. A relational database system for images. Technical Report TR-EE 79-28, Purdue University, May, 1979
[4] S. K. Chang, C. W. Yan, D. C. Dimitroff, T. Arndt. An intelligent image database system. IEEE Trans. Software Eng, 1988,14(5): 681-688
[5] K. R. Castleman..Digital image processing. Prentice Hall, Upper Saddle River, New Jersey, USA, 1996
[6] T. W. Ridler, S. Calvard. Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man and Cybernetics, August 1978, SMC-8 (8): 630-632
[7] N. Ostu. A threshold selection method from gray level histograms. IEEE Transactions on Systems, Man and Cybernetics, January 1979, SMC-9 (1): 62-66
[8] Y. Nakagawa, A. Rosenfeld. Some experiments on variable thresholding. Pattern Recognition, May 1979, 11(3): 191-204
[9] H. Derin, H. Elliott, R. Cristi, D. Geman. Bayes smoothing algorithms for segmentation of binary images modeled by markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, November 1984, PAMI-6 (6): 707-720
[10] Y. W. Lim, S. U. Lee. On the color image segmentation algorithm based on the thresholding and fuzzy c-means techniques. Pattern Recognition, September 1990, 23(9): 935-952
[11] J.M. Prager. Extracting and labeling boundary segments in natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, 2(1): 16-27
[12] Lothar Hermes, Thomas Z(?)ller, Joachim M. Buhmann. Parametric distributional clustering for image segmentation, Springer, 2002, ECCV 2002(3): 577-591
[13] Jia Wang, Wen-jann Yang, Raj Acharya. Color space quantization for color-content-based query systems. Multimedia Tools and Applications, 2001,13: 73-91
[14] Kwon, H., Kim, B., Hwang, H., Cho, D. Scale and rotation invariant pattern recognition using complex-log mapping and augmented second order neural network, IEE Electronics Letters, 1993, 29(7): 620-621
[15] John Zachary, S. S. Iyengar, Jacob Barhen. Content based image retrieval and information theory: A General Approach. Journal of the American Aociety for Information Science and Technology, 2001, 52(10): 840-852
[16] K. Idrissi, J. Ricard, A. Anwander, A. Buskurt. An image retrieval system based on local and global color descriptors. PCM 2001, 2001, 2195:55-62
[17] H. Zhang, Y. Gong, C. Y. Low, S.W. Smoliar. Image retrieval based on color features: An evaluation study. In Proc. SPIE Digital Image Storage Archiving Systems, 1995, 2606:212-220
[18] 张飙，张田文。基于代表色特征的图像检索。哈尔滨师范大学自然科学学报，2001，17(2)：57-63
[19] Yining Deng, Member, IEEE, B. S. Manjunath, Member, IEEE, Charles Kermey, Michael S. Moore, Student Member, IEEE, and Hyundoo Shin. An efficient color representation for image retrieval. IEEE transactions on image procession, JANUARY 2001, 10(1)
[20] J. Huang, S. R. Kumar, M. Mitra, W. Zhu, R. Zabih. Image indexing using color corr-elograms. In Proc. IEEE Conf. Computer Vision Pattern Recognition, 1997: 762-768.
[21] H.Y. Kwon, H.Y. Hwang. A content-based image retrieval method using third order color feature relations. IDEAL 2000, 2000, 1983:479-483
[22] Greg Pass Ramin Zabih. Comparing images using joint histograms. Computer Science Department Cornell University Ithaca, NY 14853
[23] KlAN-LEE TAN, BENG CHIN OOI, CHIA YEOW YEE. An evaluation of color spatial retrieval techniques for large image databases. Multimedia Tools and Applications, 2001, 14: 55-78
[24] Djemel Ziou, Salvatore Tabbone. Edge detection techniques an overview. Technical report, No. 195
[25] M. Tuceyran, A.K. Jain. Texture analysis, in handbook of pattern recognition and computer vision. World Scientific, Singapore: C.H. Chen, L.F. Pau and P.S.P. Wang (Eds.), 1993, chapter 2: 235-276
[26] K. Karu, A.K. Jain, R.M. Bolle, Is there any texture in the image? Pattern Recognition, 1996, 29(9): 1437-1446
[27] Junchul Chun, George Stockman. Subband image segment using vector quality for content-based image retrieval. ACM 1-581,2001
[28] Hsin-Chih Lin, Ling-Ling Wang, Shi-Nine Yang. Regular-texture image retrieval based on texture-primitive extraction. Image and Vision Computing, 1999, 17:51-63
[29] Hideyuki Tamura, Shunji Mori, Takashi Yamawaki. Texture features corresponding to visual perception. IEEE Trans. On Sys, Man, and Cyb, 1978, SMC-8(6): 460-473
[30] James Ze Wang, Gio Wiederhold, Oscar Firsehein, Sha Xin Wei. Content-based image indexing and searching using Daubeehies' wavelets. Int J Digit Libr, 1997, 1:311-328
[31] Xiang Scan Zhou, Thomas S. Huang. Edge-based structural features for content-based image retrieval. Pattern Recognition Letters April, 2001, 22(5)
[32] Ying Wu. Color, Edge and Texture.ECE510-Computer Vision Notes Series 3
[33] S. J. Park, D. K. Park, C. S. Won. Core experiments MPEG-7 edge histogram descriptor. MPEG document M5984, Geneva, May 2000
[34] ISO/IEC/JTCI/SC29/WG11. Core experiment results for edge histogram descriptor (CT4). MPEG document M6174, Beijing: July 2000
[35] Dong Kwon Park, Yoon Seok Jeon, Chee Sun Won. Efficient use of local edge histogram descriptor. ACM, ACM multimedia workshop marina del rey CA USA, 2000 1-58113-311
[36] Yong Rui, Thomas S.Huang, Shih-Fu Chang. Image retrieval: current techniques, promising directions and open issues.
[37] Dengsheng Zhang, Guojun Lu. Content-based shape retrieval using different shape descriptors: a comparative study.
[38] Dong-Ho Lee, Hyoung-Joo Kim. A fast content-based indexing and retrieval technique by the sharp information in large image database. The Journal of Systems and Software, 2001, 56: 165-182
[39] Zhiqiang Zheng, Han Wang, Eam Khwang Teoh. Analysis of gray level corner detection. Pattern Recognition Letters, 1999, 20:149-162
[40] Liu Wen-Yu, Li hua, Zhu Guang-Xi. A fast algorithm for corner detection using the morphologic skeleton. Pattern Recognition Letter, 2001, 22:891-900
[41] Nicu Sebe, Michael S. Lew. Comparing salient point detectors. Pattern Recognition Letters, 2003, 24:89-96
[42] D. B. Lomet, B. Salzberg. A robust multimedia-attribute search structure. Data Engineering, Proc. Fifth Int'l Conf., 1989:296-304
[43] S.-F. Chang. Compressed-domain techniques for image/video indexing and manipulation. Proc. ICIP95, Special Session on Digital Library and Video on Demand, 1995, Ⅰ: 314-316
[44] V. Ng, T. Kameda. Concurrent access to point data. Computer Software and Applications Conference, COMPSAC '97. Proceedings., The Twenty-First Annual International, 1997, 368-373
[45] A. Guttman. R-tree: A dynamic index structure for spatial searching. ACM SIGMOD, 1984, 47-57
[46] T. Sellis, N. Roussopoulos, C. Faloutsos. The R+-tree: A dynamic index for multi-dimensional objects. Proc. 12th VLDB, 1987, 507-518
[47] N. Beckmann, H.-P. Kriegel, R. Schneider, B. Seeger. The R+-tree: An efficient and robust access method for points and rectangles. Proc. ACM SIGMOD, 1990, 322--331
[48] D. White, R. Jain. Similarity indexing: Algorithms and performance. Proc. SPIE Storage and Retrieval for Image and Video Databases, 1996, 2670:62-73
[49] R. Ng, A. Sedighian. Evaluating multidimensional indexing structures for images transformed
by principal component analysis. Proc. SPIE Storage and Retrieval for Image and Video Databases, 2670:50-61
[50] Sameer Antania, Rangachar Kasturia, Ramesh Jainb, A surveyon the use of pattern recognition methods for abstraction, indexing and retrieval of images and video, Pattern Recognition, 2002, 35:945-965
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