Abstract
A color image segmentation technique which exploits a novel definition of rough fuzzy sets and the rough–fuzzy product operation is presented. The segmentation is performed by partitioning each block in multiple rough fuzzy sets that are used to build a lower and a upper histogram in the HSV color space. For each bin of the lower and upper histograms a measure, called τ index, is computed to find the best segmentation of the image. Experimental results show that the proposed method retains the structure of the color images leading to an effective segmentation.
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Chapron, M.: A new chromatic edge detector used for color image segmentation. In: Proc. 11th Int. Conf. on Pattern Recognition, vol. 3, pp. 311–314 (1992)
Cheng, H.D., Jiang, X.H., Wang, J.: Color image segmentation based on homogram thresholding and region merging. Pattern Recognition 35, 373–393 (2002)
Li, S.Z.: Markov Random Field Modeling in Computer Vision, Kunii, T.L. (ed.). Springer, Berlin (1995)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proc. 8th Int Conf. Computer Vision, vol. 2, pp. 416–423 (2001)
Mohabey, A., Ray, A.K.: Rough set theory based segmentation of color images. In: Proc. 19th Internat. Conf. NAFIPS, pp. 338–342 (2000)
Mushrif, M.M., Ray, A.K.: Color image segmentation: Rough-set theoretic approach. Pattern Recognition Letters 29, 483–493 (2008)
Panjwani, D.K., Healey, G.: Markov random field models for unsupervised segmentation of textured color images. IEEE Trans. Pattern Anal. Mach. Intell. 17(10), 939–954 (1995)
Pawlak, Z.: Rough sets. Int. J. of Inf. and Comp. Sci. 5, 341–356 (1982)
Pawlak, z.: Granularity of knowledge, indiscernibility and rough sets. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 106–110 (1998)
Sen, D., Pal, S.K.: Generalized Rough Sets, Entropy, and Image Ambiguity Measures. IEEE Trans. Sys. Man and Cyb. 39(1), 117–128 (2009)
Petrosino, A., Ferone, A.: Feature Discovery through Hierarchies of Rough Fuzzy Sets. In: Chen, S.M., Pedrycz, W. (eds.) Granular Computing and Intelligent Systems: Design with Information Granules of Higher Order and Higher Type (to appear, 2011)
Shafarenko, L., Petrou, M., Kittler, J.V.: Histogram based segmentation in a perceptually uniform color space. IEEE Trans. Image Process. 7(9), 1354–1358 (1998)
Trémeau, A., Colantoni, P.: Regions adjacency graph applied to color image segmentation. IEEE Trans. Image Process. 9(4), 735–744 (2000)
Uchiyama, T., Arbib, M.A.: Color image segmentation using competitive learning. IEEE Trans. Pattern Anal. Mach. Intell. 16(12), 1197–1206 (1994)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: A Measure for Objective Evaluation of Image Segmentation Algorithms. In: Proc. CVPR WEEMCV (2005)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
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Ferone, A., Pal, S.K., Petrosino, A. (2011). A Rough-Fuzzy HSV Color Histogram for Image Segmentation. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24085-0_4
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DOI: https://doi.org/10.1007/978-3-642-24085-0_4
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