SaliencyRank: Two-stage manifold ranking for salient object detection

Abstract

Salient object detection remains one of the most important and active research topics in computer vision, with wide-ranging applications to object recognition, scene understanding, image retrieval, context aware image editing, image compression, etc. Most existing methods directly determine salient objects by exploring various salient object features. Here, we propose a novel graph based ranking method to detect and segment the most salient object in a scene according to its relationship to image border (background) regions, i.e., the background feature. Firstly, we use regions/super-pixels as graph nodes, which are fully connected to enable both long range and short range relations to be modeled. The relationship of each region to the image border (background) is evaluated in two stages: (i) ranking with hard background queries, and (ii) ranking with soft foreground queries. We experimentally show how this two-stage ranking based salient object detection method is complementary to traditional methods, and that integrated results outperform both. Our method allows the exploitation of intrinsic image structure to achieve high quality salient object determination using a quadratic optimization framework, with a closed form solution which can be easily computed. Extensive method evaluation and comparison using three challenging saliency datasets demonstrate that our method consistently outperforms 10 state-of-theart models by a big margin.