A genetic algorithm for color image segmentation
作者： Alessia Amelio , Clara Pizzuti
DOI: 10.1007/978-3-642-37192-9_32
关键词:
摘要: A genetic algorithm for color image segmentation is proposed. The method represents an as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. Similarity between two pixels computed by taking into account not only brightness, but also texture content. Experiments on images from the Berkeley Image Segmentation Dataset show that able partition natural human scenes in number of regions consistent with visual perception. quantitative evaluation compared other approaches shows can be very competitive partitioning images.
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Vitoantonio Bevilacqua, Giuseppe Mastronardi, Alessandro Piazzolla, An evolutionary method for model-based automatic segmentation of lower abdomen CT images for radiotherapy planning european conference on applications of evolutionary computation. pp. 320- 327 ,(2010) , 10.1007/978-3-642-12239-2_33
Thomas Leung, Jitendra Malik, Contour Continuity in Region Based Image Segmentation european conference on computer vision. pp. 544- 559 ,(1998) , 10.1007/BFB0055689
M. Sayadi, S. Ben Chaabane, E. Brassart, F. Fnaiech, Dempster-Shafer Evidence Theory for Image Segmentation: Application in Cells Images World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering. ,vol. 3, pp. 2648- 2654 ,(2009)
Lucia Ballerini, Leonardo Bocchi, Carina B. Johansson, Image Segmentation by a Genetic Fuzzy c-Means Algorithm Using Color and Spatial Information Lecture Notes in Computer Science. pp. 260- 269 ,(2004) , 10.1007/978-3-540-24653-4_27
C.T. Zahn, Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters IEEE Transactions on Computers. ,vol. C-20, pp. 68- 86 ,(1971) , 10.1109/T-C.1971.223083
Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Efficient Graph-Based Image Segmentation International Journal of Computer Vision. ,vol. 59, pp. 167- 181 ,(2004) , 10.1023/B:VISI.0000022288.19776.77
Roderick Urquhart, Graph theoretical clustering based on limited neighbourhood sets Pattern Recognition. ,vol. 15, pp. 173- 187 ,(1982) , 10.1016/0031-3203(82)90069-3
Y.J. Zhang, Evaluation and comparison of different segmentation algorithms Pattern Recognition Letters. ,vol. 18, pp. 963- 974 ,(1997) , 10.1016/S0167-8655(97)00083-4
Ying Xu, Victor Olman, Edward C. Uberbacher, A segmentation algorithm for noisy images: design and evalution Pattern Recognition Letters. ,vol. 19, pp. 1213- 1224 ,(1998) , 10.1016/S0167-8655(98)00102-0
William M. Rand, Objective Criteria for the Evaluation of Clustering Methods Journal of the American Statistical Association. ,vol. 66, pp. 846- 850 ,(1971) , 10.1080/01621459.1971.10482356