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...

Abstract

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.

Viewing alternatives

Look up in Google Scholar

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions
No digital document available to download for this item

Item Actions

Export

About

CORE (COnnecting REpositories)

Recommendations