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Fuzzy Clustering for Image Segmentation Using Generic Shape Information

Ali, Ameer; Karmakar, Gour C. and Dooley, Laurence S. (2008). Fuzzy Clustering for Image Segmentation Using Generic Shape Information. Malaysian Journal of Computer Science, 21(2) pp. 122–138.

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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 Article
Copyright Holders: 2008 Unknown
ISSN: 0127-9084
Keywords: image segmentation, generic shape, fuzzy clustering, B-spline
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 12945
Depositing User: Laurence Dooley
Date Deposited: 26 Jan 2009 09:01
Last Modified: 24 Feb 2016 01:00
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