Graph-Based Salient Object Detection using Background and Foreground Connectivity Cues

Abstract

Salient object detection is an active research topic due to several potential applications in image compression, scene understanding, image retrieval, and so forth. In this paper, a salient object detection method is proposed by leveraging the recent advances in graph signal processing. Since, the image boundary regions generally belong to the image background, a distribution-based boundary contrast map is generated. Also, the graph representation of the image is used to compute the connectivity of the image regions to the image boundary as well as those to their local neighbors and the image foreground. The connectivity maps obtained are fused with the boundary contrast map in order to obtain the image saliency map. Several experiments are conducted to evaluate the performance of the proposed salient object detection method and to compare it with the state-of-the-arts. Results on datasets of images demonstrate that the proposed method achieves superior performance to the state-of-the-art methods in terms of precision, recall, and mean absolute error values.