Title: A multi-agent system architecture to classify colour images

Authors: Danni Ai; Mohammad Khazab; Jeffrey W. Tweedale; Lakhmi C. Jain; Yen-Wei Chen

Addresses: Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, South Australia, 5095, Australia; Graduate School of Science and Engineering, Ritsumeikan University, Biwako-Kusatsu Campus, 525-8577 Kusatsu, Shiga Nojihigashi, Japan ' Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, South Australia, 5095, Australia; Graduate School of Science and Engineering, Ritsumeikan University, Biwako-Kusatsu Campus, 525-8577 Kusatsu, Shiga Nojihigashi, Japan ' Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, South Australia, 5095, Australia; Graduate School of Science and Engineering, Ritsumeikan University, Biwako-Kusatsu Campus, 525-8577 Kusatsu, Shiga Nojihigashi, Japan ' Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, South Australia, 5095, Australia; Graduate School of Science and Engineering, Ritsumeikan University, Biwako-Kusatsu Campus, 525-8577 Kusatsu, Shiga Nojihigashi, Japan ' Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, South Australia, 5095, Australia; Graduate School of Science and Engineering, Ritsumeikan University, Biwako-Kusatsu Campus, 525-8577 Kusatsu, Shiga Nojihigashi, Japan

Abstract: Colour image classification plays an important role in computer vision and pattern recognition. Traditional classification research mainly focuses on developing novel techniques that are efficient for image representation or classification. By processing considerable visual information, human can handle the complicated classification tasks quite effectively. Inspirited by the structure of the visual cortex, we propose a multi-agent colour image classification architecture (MACICA). Agents within a multi-agent system (MAS) architecture are programmed to deliver specific image classification capabilities. The MACICA provides an efficient classification output by sharing knowledge, communication and team work. The architecture is flexible and dynamic, while the platform has produced encouraging results, which are presented in the paper.

Keywords: multi-agent systems; MAS; colour images; image classification; agent-based systems; computer vision; pattern recognition.

DOI: 10.1504/IJAIP.2013.058304

International Journal of Advanced Intelligence Paradigms, 2013 Vol.5 No.4, pp.284 - 298

Published online: 30 Jul 2014 *

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