Object-Based Image Segmentation using Fuzzy Clustering

Ali, M. A; Karmakar, G. C. and Dooley, L. S. (2006). Object-Based Image Segmentation using Fuzzy Clustering. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’06), 14-19 May 2006, Toulouse.

DOI: https://doi.org/10.1109/ICASSP.2006.1660290

URL: http://ieeexplore.ieee.org/search/wrapper.jsp?arnu...


Existing shape-based clustering algorithms, including fuzzy k-rings, fuzzy k-elliptical, circular c-shell, and fuzzy c-shell ellipsoidal are all designed to segment regular geometrically shaped objects such as circles, ellipses or combination of both. These algorithms however, are unsuitable for segmenting arbitrary-shaped objects, so in an attempt to address this issue, a fuzzy image segmentation of generic shaped clusters (FISG) algorithm was introduced that integrated generic shape information into the segmentation framework. It however, had a number of limitations relating to the mathematical derivation of the updated contour radius, the initial shape representation, and the impact of overlapping clusters. This paper proposes a new object based segmentation using fuzzy clustering (OSF) algorithm that solves these drawbacks by controlling the scaling of original shape, securing a better initial shape representation and avoids cluster overlapping, with both qualitative and quantitative results confirming the improved overall segmentation performance.

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