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C-Means with Fuzzy Local Information

Publication Type : Journal Article

Publisher : Data Mining and Knowledge Engineering

Source : Data Mining and Knowledge Engineering, Volume 3, Number 16, p.952–960 (2011)

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Keywords : Clustering, Fuzzy C-Means, Fuzzy Constraints, Graylevel Constraints, Image segmentation, Spatial Constraints.

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2011

Abstract : This paper presents a variation of Fuzzy c-Means(FCM )algorithm that provides image clustering .The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way.The new algorithm is called C-Means with Fuzzy Local Information (CMFLI). CMFLI can overcome the disadvantages of known fuzzy c-means algorithm and at the same time enhances the clustering performance.The major characteristic of CMFLI is the use of fuzzy local information(both spatial and gray level)similarity measure, aiming to guarantee noise insensitiveness and image detail preservation .Furthermore, the proposed algorithm is fully free of empirically adjusted parameters (a, λg , λs etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature.Experiments performed on synthetic and real world images show that CMFLI algorithm is efficient,providing robustness to noisy images.

Cite this Research Publication : R. P Devi, Dr. Madhu Mohan N., V Kumar, P., and A Rao, N., “C-Means with Fuzzy Local Information”, Data Mining and Knowledge Engineering, vol. 3, pp. 952–960, 2011.

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