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Selforganizology, 2016, 3(3): 87-99
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Article

An efficient algorithm for color image segmentation

Neha Bhardwaj, Arun Solanki
Department of Computer Science and Engineering, School of Information and Communication Technology, Gautam Buddha University Greater Noida, India

Received 11 April 2016;Accepted 20 May 2016;Published online 1 September 2016
IAEES

Abstract
In field of image processing, image segmentation plays an important role that focus on splitting the whole image into segments. Representation of an image so that it can be more easily analysed and involves more information is an important segmentation goal. The process of partitioning an image can be usually realized by Region based, Boundary based or edge based method. In this work a hybrid approach is followed that combines improved bee colony optimization and Tabu search for color image segmentation. The results produced from this hybrid approach are compared with non-sorted particle swarm optimization, non-sorted genetic algorithm and improved bee colony optimization. Results show that the Hybrid algorithm has better or somewhat similar performance as compared to other algorithms that are based on population. The algorithm is successfully implemented on MATLAB.

Keywords image segmentation;improved bee colony optimization;Tabu search;non-dominated sorted particle swarm optimization;non-dominated sorted genetic algorithm.

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