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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414

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Color Image Segmentation Using SVM Pixel Classification Image
The goal of image segmentation is to cluster pixels into salient image regions. Segmentation could be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing, or image database lookup. In this paper, we present a color image segmentation using support vector machine (SVM) pixel classification. Firstly, the pixel level color and texture features of the image are extracted and they are used as input to the SVM classifier. These features are extracted using the homogeneity model and Gabor Filter. With the extracted pixel level features, the SVM Classifier is trained by using FCM (Fuzzy C-Means).The image segmentation takes the advantage of both the pixel level information of the image and also the ability of the SVM Classifier. The Experiments show that the proposed method has a very good segmentation result and a better efficiency, increases the quality of the image segmentation compared with the other segmentation methods proposed in the literature.
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[1] X. Haixiang, C. Wanhua, C. Wei, C and G. Liyuan, “Performance evaluation of SVM in image segmentation”, In 2008 9th International Conference on Signal Processing, pp. 1207-1210, 2008.
[2] V. Vapnik, “The nature of statistical learning theory”, springer., New York, 2000.
[3] J.E. Francisco, D.J and Allan, “Benchmarking image segmentation algorithms”, International Journal of Computer Vision, vol.85, no.2, pp.167–181, 2009.
[4] R. Unnikrishnan, C.E. Pantofaru and M. Hebert, “Toward objective evaluation of image segmentation algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.6, pp. 929–943, 2007.
[5] M. Song and D. Civco, “Road extraction using SVM and image segmentation”, Photogrammetric Engineering & Remote Sensing, vol. 70, no. 12, pp. 1365-1371, 2004.
[6] J.J. Quan and X.B. Wen, “Multiscale probabilistic neural network method for SAR image segmentation”, Applied Mathematics and Computation,vol. 205, no.2,,pp.578–583, 2008.
[7] X. Wang and Y. Sun, “A color- and texture-based image segmentation algorithm”, Machine Graphics & Vision, vol.19, no.1, pp. 3–18, 2010.
[8] A. H. Yu, and C.C. Chang, “Scenery image segmentation using support vector machines”, Fundamenta Informaticae, vol. 61, no. 3, pp. 379- 388, 2004.
[9] J. Shi and J. Malik, “Normalized cuts and image segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no.8, pp.888–905, 2000.
[10] M. Pabitra, B.U. Shankar and K.P. Sankar, “Segmentation of multispectral remote sensing images using active support vector machines”, Pattern Recognition Letters, vol.25, no.9, pp. 1067–1074, 2004.
[11] J, Yan and J. Zheng, “One-class SVM based segmentation for SAR image”, In Advances in Neural Networks–ISNN 2007, pp. 959–996, 2007.
[12] B. Cyganek, “Color image segmentation with support vector machines:applications to road signs detection”, International Journal of Neural Systems, vol.18, no.4, pp. 339–345, 2008.
[13] J.J. Huang, G.H. Tzeng and C. S. Ong, “Marketing segmentation using support vector clustering”, “Expert systems with applications”, vol. 32, no. 2, pp. 313-317, 2007.
[14] J. Ji, F. Shao, R. Sun, N. Zhang and G. Liu, “A TSVM based semisupervised approach to SAR image segmentation”. In Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing, International Workshop, IEEE, Vol. 1, pp. 495-498, 2008.
[15] Z. Xue, L. Long, S. Antani, G.R. Thoma and J. Jeronimo, “Segmentation of mosaicism in cervicographic images using support vector machines” Proc SPIE Med Imaging, vol. 7259, no.1 pp. 72594X–72594X-10, 2009.
[16] B. Boser, I. Guyon, and V. Vapnik, “An training algorithm for optimal margin classifiers”, In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152,1992.
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