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An Edge-Based Color Image Retrieval by Using Multiple Features |
WANG Xiang-Yang1,2,CHEN Jing-Wei1,YU Yong-Jian1 |
1.School of Computer and Information Technology,Liaoning Normal University,Dalian 116029 2.State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093 |
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Abstract Feature extraction and representation is one of the most important techniques in the content-based image retrieval (CBIR). An image edge is the boundary between an object and the background and indicates the boundary between overlapping objects. In this paper, an edge-based color image retrieval by using multiple features is proposed. Firstly, the color edge is extracted by using canny detection operator. Secondly, the weighted color histogram, the angle histogram and the gradient orientation histogram about the extracted color edge image are computed as image features. Finally, the similarity between color images is computed by combined feature index based on three kinds of histograms. Experimental results show that the proposed image retrieval is accurate and efficient in the user-interested images retrieval.
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Received: 13 January 2009
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[1] Datta R, Joshi D, Li Jia, et al. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys, 2008, 40(2): 1-60 [2] Michael S L, Nice S, Chababe D, et al. Content-Based Multimedia Information Retrieval: State of the Art and Challenges. ACM Trans on Multimedia Computing, Communications and Applications, 2006, 2(1): 1-19 [3] Swain M J, Ballard D H. Color Indexing. International Journal of Computer Vision, 1991, 7(1): 11-32 [4] Li Xuelong. Image Retrieval Based on Perceptive Weighted Color Blocks. Pattern Recognition Letters, 2003, 24(12): 1935-1941 [5] Wang Yi, Zhai Hongchen, Liang Yanmei, et al. Shape Description Matrix and Its Applications to Color-Image Retrieval and Recognition. Science in China: Series E, 2004, 34(3): 337-344 (in Chinese) (王 熠,翟宏琛,梁艳梅,等.形态描述矩阵及其在彩色图像检索
与识别中的应用.中国科学:E辑, 2004, 34(3): 337-344) [6] Jeong S, Won C S, Gray R M. Image Retrieval Using Color Histograms Generated by Gauss Mixture Vector Quantization. Computer Vision and Image Understanding, 2004, 94(1/2/3): 44-66 [7] Stttinger J, Sebe N, Gevers T, et al. Colour Interest Points for Image Retrieval // Proc of the 12th Computer Vision Winter Workshop. Hubli, India, 2007: 83-90 [8] Haralick R M, Shanmngam K, Dinstein I. Texture Feature for Image Classification. IEEE Trans on Systems, Man and Cybernetics, 1973, 3(6): 610-621 [9] Tarmura H, Mori S, Yamawaki T. Texture Features Corresponding to Visual Perception. IEEE Trans on Systems, Man and Cybernetics, 1978, 8(6): 460-473 [10] Manthalkar R, Biswas P K, Chatterji B N. Rotation and Scale Invariant Texture Features Using Discrete Wavelet Packet Transform. Pattern Recognition Letters, 2003, 24(14): 2455-2462 [11] Kourosh J K, Hamid S Z. Rotation-Invariant Multiresolution Texture Analysis Using Radon and Wavelet Transform. IEEE Trans on Image Processing, 2005, 14(6): 783-795 [12] Han Ju, Ma K K. Rotation-Invariant and Scale-Invariant Gabor Features for Texture Image Retrieval. Vision and Image Computing, 2006, 25(9): 1474-1481 [13] Pun C M, Lee M C. Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 590-603 [14] Gluckman J. Visually Distinct Patterns with Matching Subband Statistics. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(2): 252-264 [15] Zhang Dengsheng, Lu Guojun. Review of Shape Representation and Description Techniques. Pattern Recognition, 2004, 37(1): 1-19 [16] Xu Xiaoqian, Lee D J, Antani S, et al. A Spine X-Ray Image Retrieval System Using Partial Shape Matching. IEEE Trans on Information Technology in Biomedicine, 2008, 12(1): 100-108 [17] Wei C H, Li Y, Chau W Y, et al. Trademark Image Retrieval Using Synthetic Features for Describing Global Shape and Interior Structure. Pattern Recognition, 2009, 42(3): 386-394 [18] Hiremath P S, Pujari J. Content Based Image Retrieval Using Color, Texture and Shape Features // Proc of the 15th International Conference on Advanced Computing and Communications. Guwahati, India, 2007: 780-784 [19] Canny J. A Computational Approach to Edge-Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698 |
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