Detecting Salient Objects via Spatial and Appearance Compactness Hypotheses

Object-level saliency detection has been attracting a lot of attention, due to its potential enhancement in many high-level vision tasks. Many previous methods are based on the contrast hypothesis which regards the regions with high contrast in a certain context as salient. Although the contrast hypothesis is valid in many cases, it cannot handle some difficult cases. To make up for the weakness of contrast hypothesis, we propose a novel compactness hypothesis which assumes salient regions are more compact than background spatially and in appearance. Based on compactness hypotheses, we implement an effective object-level saliency detection method, which is demonstrated to be effective even in difficult cases. In addition, we present an adaptive multiple saliency maps fusion framework which can automatically select saliency maps of high quality according to three quality assessment rules. We evaluate the proposed method on four benchmark datasets and the comparable performance as the state-of-the-art methods has been achieved.