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Image segmentation is a classic inverse problem which consists in obtaining a compact, region-based description of the image scene by decomposing it into meaningful or spatially coherent regions sharing similar attributes. Because a color image can provide more perceptual information, color image segmentation is being paid more and more attention. In this paper, we propose a new approach to color image segmentation that is based on low-level features of color and texture. The approach is aimed at segmentation of natural scenes where the color and texture of each segment do not typically exhibit uniform statistical characteristics. Firstly, local color composition is described in terms of spatially adaptive dominant colors by using the Gibbs random field, and the color image is segmented into regions according to the local color composition. Secondly, the texture characteristics of the grayscale component are described by utilizing the Steerable filter, and the grayscale component of color image is cut into flat regions and non-flat regions. Thirdly, the local color composition and texture characteristics are combined to obtain an overall crude segmentation. Finally, an elaborate border refinement procedure is used to obtain accurate and precise border localization by appropriately combining color-texture features with the Normalized Cuts. The experimental results demonstrate that the color image segmentation results of the proposed approach exhibit favorable consistency in terms of human perception.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
3--18
Opis fizyczny
Bibliogr. 19 poz., il., wykr.
Twórcy
autor
autor
- School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China, wxy37@126.com
Bibliografia
- [1] W. T. Freeman, E. H. Adelson. The design and use of Steerable filters. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(9): 891-90, 1991.
- [2] J. Shi, J. Malik. Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(8): 888-905, 2000
- [3] A. Mojsilovic, J. Hu, E. Soljanin. Extraction of perceptually important colors and similarity measurement for image matching, retrieval, and analysis. IEEE Trans. on Image Processing, 11(11): 1238-1248, 2002.
- [4] T. Chaira, A. K. Ray. Segmentation using fuzzy divergence. Pattern Recognition Letter, 24: 837-1844, 2003.
- [5] M. O. Baradez, et al. Robust and automated unimodal histogram thresholding and potential applications. Pattern Recognition, 37(6): 1131-1148, 2004.
- [6] X. Y. Zeng, Y. W. Chen, Z. Nakao, H. Q. Lu. Texture representation based on pattern map. Signal Processing, 84(3): 589-599, 2004.
- [7] Alan Wee-Chung Liew, Image segmentation based on adaptive cluster prototype estimation. IEEE Trans. on Fuzzy Systems, 13(4): 444-453, 2005.
- [8] J. Q. Chen. Adaptive perceptual color-texture image segmentation. IEEE Trans. on Image Processing, 14(10): 1524-1536, 2005.
- [9] P. Bao, L. Zhang, X. Wu. Canny edge detection enhancement by scale multiplication. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(9): 1485-1490, 2005.
- [10] Qiming Luo, Taghi M. Khoshgoftaar. Unsupervised multiscale color image segmentation based on MDL principle. IEEE Trans. Image Processing, 15(9): 2755-2761, 2006.
- [11] J. H. Wang, F. C. Chang, F. W. Su. Image segmentation via self- organizing fusion. IEE Proc.-Vis. Image Signal Process, 153(5): 657-665, 2006.
- [12] R. Unnikrishnan, C. E. Pantofaru, M. Hebert. Toward objective evaluation of image segmentation algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence, 29(6): 929-943, 2007.
- [13] Christophoros Nikou. A class-adaptive spatially variant mixture model for image segmentation. IEEE Trans. on Image Processing, 16(4): 1121-1130, 2007.
- [14] Wenbing Tao, Hai Jin. Color image segmentation based on mean shift and normalized cuts. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, 37(5): 1382-1389, 2007.
- [15] L. Ma, R. C. Staunton, A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns, Pattern Recognition, 40(11): 3005-3011, 2007.
- [16] Z. Petera, V. Boussone, C. Bergote,g, F. Peyrina. A constrained region growing approach based on watershed for the segmentation of low contrast structures in bone micro-CT images. Pattern Recognition. 41: 2358-2368, 2008.
- [17] J. J. Quan, X. B. Wen. Multiscale probabilistic neural network method for SAR image segmentation. Applied Mathematics and Computation, 205(2): 578-583, 2008.
- [18] X. Fan, G. P. Zhang, X. Z. Xia. Performance evaluation of SVM in image segmentation. IEEE International Workshop on Semantic Computing and Applications, 160-165, 2008.
- [19] Kuo-Liang Chung, Wei-Jen Yang, Wen-Ming Yan: Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation. J. Visual Communication and Image Representation, 19(5): 299-310, 2008.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BWA9-0039-0022