Title: A heuristic optimisation approach for semantic-based image retrieval using relevance feedback
Authors: Nilesh P. Bhosle; Manesh B. Kokare
Addresses: Department of Electronics and Telecommunication, Zeal College of Engineering and Research, Pune 411041, India ' Department of Electronics and Telecommunication, SGGS Institute of Engineering and Technology, Vishnupuri, Nanded 431606, India
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.
Keywords: content-based image retrieval; CBIR; semantic gap; relevance feedback; feature relevance weighting; FRW; query point movement; QPM; bacterial foraging optimisation; BFO; metaheuristics.
International Journal of Applied Pattern Recognition, 2016 Vol.3 No.4, pp.293 - 307
Received: 12 Jan 2016
Accepted: 17 Mar 2016
Published online: 13 Feb 2017 *