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Image Segmentation Based on Bacterial Foraging and FCM Algorithm

Image Segmentation Based on Bacterial Foraging and FCM Algorithm

Hongwei Mo, Yujing Yin
Copyright: © 2011 |Volume: 2 |Issue: 3 |Pages: 13
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781613509333|DOI: 10.4018/jsir.2011070102
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MLA

Mo, Hongwei, and Yujing Yin. "Image Segmentation Based on Bacterial Foraging and FCM Algorithm." IJSIR vol.2, no.3 2011: pp.16-28. http://doi.org/10.4018/jsir.2011070102

APA

Mo, H. & Yin, Y. (2011). Image Segmentation Based on Bacterial Foraging and FCM Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 2(3), 16-28. http://doi.org/10.4018/jsir.2011070102

Chicago

Mo, Hongwei, and Yujing Yin. "Image Segmentation Based on Bacterial Foraging and FCM Algorithm," International Journal of Swarm Intelligence Research (IJSIR) 2, no.3: 16-28. http://doi.org/10.4018/jsir.2011070102

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Abstract

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

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