Fuzzy Content-Based Image Retrieval Speed-up Using the Multi-Agent Platform

Document Type : Research Article

Author

Department of computer enginering and information technology, Payame Noor University (PNU), Iran.

Abstract

Parallelization is a technique that increases the speed of tasks by processing tasks simultaneously. Distributed multi-agent systems are one of the cases in which parallel processing techniques can be used. In this paper, first, the multi-agent model of a fuzzy content-based image retrieval system is designed to distribute it. Then the corresponding parallel multi-agent model is designed. Then the parallel image retrieval system is implemented on reconfigurable hardware. This method is based on a multi-agent paradigm which is suitable for various parallelisms within applied problems. In this study, by using parallelism techniques, a method is presented that decreases considerably the consumption time of image retrieval systems.

This paper focuses on how to implement a fuzzy content-based image retrieval system, in the form of a multi-agent model. Because reconfigurable hardware is appropriate to support software agents, I also show how these agents use the inherent parallelism of reconfigurable Hardware for parallel image retrieval and increase the speed of this new system greatly. The two sequential and parallel systems are tested on a data set which is containing 1000 images. The results signify the increase of almost 3-times speed for the proposed parallel system by software agents and 400-times speed by hardware agents. In order to evaluate the retrieval efficiency of the proposed system compared to previous works, two other image retrieval systems have been implemented and the efficiency and memory consumption of the systems have been compared. The results indicate better performance of the proposed system than other methods studied.

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