Multi-joint histogram based modelling for image indexing and retrieval

Abstract

This paper presents a novel approach for image retrieval, named multi-joint histogram based modelling (MJHM), in which the joint correlation histograms are constructed between the motif and texton maps. Firstly, the quantized image is divided into non-overlapping 2 × 2 grids. Then each grid is replaced by a scan motif and texton values to construct the transformed motif and texton maps (images) respectively. The motif transformed map minimizes the local gradient and texton transformed map identifies the equality of grayscales while traversing the 2 × 2 grid. Finally, the correlation histograms are constructed between the transformed motif and texton maps. The performance of the proposed method (MJHM) is tested by conducting two experiments on Corel-5K and Corel-10K benchmark databases. The results after investigation show significant improvements in terms of precision, average retrieval precision (ARP), recall and average retrieval rate (ARR) as compared to multi-texton histogram (MTH), smart content based image retrieval system (CMCM) and other state-of-the-art techniques for image retrieval. © 2014 Elsevier Ltd. All rights reserved.