Visual Saliency Fusion Based Multi-feature for Semantic Image Retrieval

Abstract

In this paper, a saliency fusion based content-based image retrieval method is proposed. Different saliency detection methods were conducted firstly and the output saliency maps were fused by double low rank matrix recovery method. Then the images were segmented into foreground and background according to the fusion result. As the foreground and background had the different impacts on the semantic understanding of the image, different features represented in the form of histogram were extracted. Finally, a fusion of z-score normalized Chi-Square distance is adopted as the similarity measurement. This proposal has been implemented on three widely used benchmark databases and the results evaluated in terms of mean Average Precision (mAP), precision, recall, and F1-measure show that our proposal outperforms the referred state-of-the-art approaches.