Fuzzy C-Means Cluster Segmentation Algorithm Based on Bacterial Colony Chemotaxis

Yan Ling Li; Gang Li

doi:10.4028/www.scientific.net/AMR.421.465

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 421Fuzzy C-Means Cluster Segmentation Algorithm Based...

Fuzzy C-Means Cluster Segmentation Algorithm Based on Bacterial Colony Chemotaxis

Abstract:

Fuzzy C-Means(FCM) algorithm is one of the most popular methods for image segmentation, but it is in essence a technology of searching local optimal solution. The algorithm’s initial clustering centers are stochastic selection which causes it to depend on the selection of the initial cluster centers excessively. For this reason, fuzzy C-means cluster segmentation algorithm based on bacterial colony chemotaxis (BCC) is proposed in this paper. Firstly, initial cluster centers of FCM algorithm is get by BCC algorithm. Then, the images are segmented using FCM algorithm. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and can provide more robust segmentation results.

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Periodical:

Advanced Materials Research (Volume 421)

Pages:

465-469

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.421.465

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Online since:

December 2011

Authors:

Yan Ling Li, Gang Li

Keywords:

Bacterial Chemotaxis Algorithm, Bacterial Colony Chemotaxis Algorithm, Fuzzy C-Means (FCM), Image Segmentation

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