Color image segmentation by cuckoo search

Nandy, Sudarshan, Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556, Sarkar, Partha Pratim and Das, Achintya (2015) Color image segmentation by cuckoo search. Intelligent Automation & Soft Computing, 21 (4). pp. 673-685. ISSN 1079-8587

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Abstract

In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.

Item Type: Article
Additional Information: Published online: 16 Apr 2015
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19347
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 19 Apr 2016 09:57
Last Modified: 10 Jun 2019 13:07
URI: https://eprints.mdx.ac.uk/id/eprint/19347

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