Data Mining and Knowledge Engineering

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C-Means with Fuzzy Local Information


Affiliations
1 PVP Siddartha Institute of Engineering and Technology, University of JNTUK, India
2 Pragati Engineering College, University of JNTUK, India
3 Central University of Hyderabad, India
4 Dept. CSE, Pragati Engineering College, University of JNTUK, Surampalem, India
     

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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.

Keywords

Clustering, Fuzzy C-Means, Fuzzy Constraints, Graylevel Constraints, Image Segmentation, Spatial Constraints.
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  • C-Means with Fuzzy Local Information

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Authors

P. Rukmini Devi
PVP Siddartha Institute of Engineering and Technology, University of JNTUK, India
N. Mohan
Pragati Engineering College, University of JNTUK, India
V. Praveen Kumar
Central University of Hyderabad, India
A. Nageswara Rao
Dept. CSE, Pragati Engineering College, University of JNTUK, Surampalem, India

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.

Keywords


Clustering, Fuzzy C-Means, Fuzzy Constraints, Graylevel Constraints, Image Segmentation, Spatial Constraints.