Salient Object Detection Based on Context and Location Prior

A novel automatic salient object detection algorithm, which integrates context-based saliency with location computation based on the boundary priors, is proposed. Input image is expressed as a close-loop graph with superpixels as nodes and salient object of image has a well-defined graph-based manifold ranking location. The saliency of the image elements is defined based on their relevances to the given seeds or queries. Saliency object location is carried out in a two-stage scheme to extract background regions and foreground salient objects efficiently. We introduce a location weight to measure the relationship of superpixels and the centroid of the detected salient regions to eliminate the background. Saliency map is computed through context analysis and location computing based on multi-scale superpixels. Experimental results on three public benchmark datasets demonstrate that our approach performs well compared to existing state-of-the-art methods.