出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：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 shapebased clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarilyshaped objects. To address this limitation, this paper presents a new shapebased algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the Bspline representation of an object’s shape in combination with the GustafsonKessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to wellestablished shapebased clustering techniques, for a wide range of test images comprising various regular and arbitraryshaped objects.
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