A multi-agent system architecture to classify colour images

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.