Multilevel image thresholding selection using the artificial bee colony algorithm

Authors:
Ming-Huwi Horng;Ting-Wei Jiang
Affiliations:
Department of Computer Science and Information Engineering, National Pingtung Institute of Commerce, PingTung, Taiwan;Department of Computer Science and Information Engineering, National Pingtung Institute of Commerce, PingTung, Taiwan
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Year:
2010

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Abstract

Image thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied. A new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed in this paper called the maximum entropy based artificial bee colony thresholding (MEABCT) method. Three different methods, such as the methods of particle swarm optimization, HCOCLPSO and honey bee mating optimization are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Meanwhile, the results using the MEABCT algorithm is the best and its computation time is relatively low compared with other four methods.