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Fuzzy Clustering for Image Segmentation Using Generic Shape Information

Ameer Ali, M., and Karmakar, G.C., and Dooley, L.S., (2008) Fuzzy Clustering for Image Segmentation Using Generic Shape Information. Malaysian Journal of Computer Science, 21 (2). ISSN 0127-9084

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Official URL: http://ejum.fsktm.um.edu.my/ArticleInformation.aspx?ArticleID=675

Affiliations

East West University, Bangladesh. Dept. of ECE
Monash University, Australia. Gippsland School of Information Technology
Open University, Milton Keynes, UK. Dept. of Communication and Systems

Abstract

The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object’s shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects.

Item Type:Journal
Keywords:Image Segmentation, Generic Shape, Fuzzy Clustering, B-spline
Subjects:Q Science, Computer Science
ID Code:5025

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