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Performance characterization of clustering algorithms for colour image segmentation

Ilea, Dana E. and Whelan, Paul F. and Ghita, Ovidiu (2006) Performance characterization of clustering algorithms for colour image segmentation. In: OPTIM 2006 - 10th International Conference on Optimization of Electrical and Electronic Equipment, 18-19 May 2006, Brasov, Romania.

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Abstract

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied on a large suite of mosaic images.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:colour image segmentation; fuzzy clustering; competitive agglomeration; diffusion based filtering; image segmentation;
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:4665
Deposited On:03 Jul 2009 11:14 by DORAS Administrator. Last Modified 03 Jul 2009 11:14

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