Abstract
Image segmentation is a classical problem in the area of image processing, multimedia, medical image, and so on. Although there exist a lot of approaches to perform image segmentation, few of them study the image segmentation by the cluster ensemble approach. In this paper, we propose a new algorithm called the cluster ensemble algorithm (CEA) for image segmentation. Specifically, CEA first obtains two set of segmented regions which are partitioned by EM according to the color feature and the texture feature respectively. Then, it integrates these regions to k segmented regions based on the similarity measure and the fuzzy membership function. Finally, CEA performs the denoise algorithm on the segmented regions to remove the noise. The experiments show that CEA works well during the process of image segmentation.
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Yu, Z., Zhang, S., Wong, HS., Zhang, J. (2007). Image Segmentation Based on Cluster Ensemble. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_110
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DOI: https://doi.org/10.1007/978-3-540-72395-0_110
Publisher Name: Springer, Berlin, Heidelberg
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