Retrieving images using saliency detection and graph matching

Abstract

The need for fast retrieving images has recently increased tremendously in many application areas (biomedicine, military, commerce, education, etc.). In this work, we exploit the saliency detection to select a group of salient regions and utilize an undirected graph to model the dependency among these salient regions, so that the similarity of images can be measured by calculating the similarity of the corresponding graphs. Identification of salient pixels can decrease interferences from irrelevant information, and make the image representation more effective. The introduction of the graph model can better characterize the spatial constraints among salient regions. The comparison experiments are carried out on the three representative datasets publicly available (Holidays, UKB, and Oxford 5k), and the experimental results show that the integration of the proposed method and the SIFT-like local descriptors can better improve the existing state-of-the-art retrieval accuracy.