Color quantization using modified artificial fish swarm algorithm

Authors:
Danial Yazdani;Hadi Nabizadeh;Elyas Mohamadzadeh Kosari;Adel Nadjaran Toosi
Affiliations:
Department of Electronic, Computer and Information Technology, Azad University of Qazvin, Iran;Department of Electronic, Computer and Information Technology, Azad University of Qazvin, Iran;Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;Department of Computer Science and Software Engineering, University of Melbourne, Melbourne, Australia
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Abstract

Color quantization (CQ) is one of the most important techniques in image compression and processing. Most of quantization methods are based on clustering algorithms. Data clustering is an unsupervised classification technique and belongs to NP-hard problems. One of the methods for solving NP-hard problems is applying swarm intelligence algorithms. Artificial fish swarm algorithm (AFSA) fits in the swarm intelligence algorithms. In this paper, a modified AFSA is proposed for performing CQ. In the proposed algorithm, to improve the AFSA's efficiency and remove its weaknesses, some modifications are done on behaviors, parameters and the algorithm procedure. The proposed algorithm along with other multiple known algorithms has been used on four well known images for doing CQ. Experimental results comparison shows that the proposed algorithm has acceptable efficiency.