Fuzzy Clustering for Image Segmentation Using Generic Shape Information

Malaysian Journal of Computer Science

Laurence S. Dooley¹  Gour C. Karmakar¹  M. Ameer Ali¹ 

关键词: Image Segmentation;    Generic Shape;    Fuzzy Clustering;    B-spline;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: 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 toinvestigate 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-basedalgorithm 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 comprisingvarious regular and arbitrary-shaped objects.

【 授权许可】

Unknown