Ant Colony Optimization for Use in Content Based Image Retrieval

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Author(s): Konstantinos Konstantinidis (Democritus University of Thrace, Greece), Georgios Ch. Sirakoulis (Democritus University of Thrace, Greece)and Ioannis Andreadis (Democritus University of Thrace, Greece)
Copyright: 2009
Pages: 21
Source title: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies
Source Author(s)/Editor(s): Hongwei Mo (Harbin Engineering University, China)
DOI: 10.4018/978-1-60566-310-4.ch018

Keywords: Biologically-Inspired Computing / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony optimization and fuzzy logic. This method utilizes a two-stage fuzzy modified ant colony algorithm employing in parallel low-level features such as color, texture and spatial information which are extracted from the images themselves. The results prove the system to be more efficient compared to popular and contemporary methods such as the histogram intersection, joint histograms and the scalable color histogram of the MPEG-7 standard. However, due to the high computational burden of the AI methods the system is quite slow when implemented in software. Thus in order to speed up the whole process the reader is also provided with the hardware implementation analysis of the whole system. The increase in speed is phenomenal.

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Ant Colony Optimization for Use in Content Based Image Retrieval

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