A heuristic optimisation approach for semantic-based image retrieval using relevance feedback

Abstract

Relevance feedback has been considered as the most vivid approach for reduction of semantic gap in content-based image retrieval systems. However, existing relevance feedback techniques require more number of feedback iterations to fulfil user's requirement. To address the problem of convergence speed in relevance feedback, this paper proposes a novel approach using bacterial foraging optimisation algorithm. The proposed approach combines the query point movement and feature relevance weighting techniques in relevance feedback. The feature weights in feature relevance weighting technique are obtained using a heuristic approach based on bacterial foraging optimisation algorithm. The proposed system is tested on two different image databases. The experimental results confirm the high accuracy and effectiveness of the proposed system as compared to other content-based image retrieval systems available in the literature.