Object-Level saliency detection combining the contrast and spatial compactness hypothesis
Weiqiang Wang, Xiaoqian Liu, Chi Zhang · 2013
Object-level saliency detection is an important branch of visual saliency. Most previous methods are based on the contrast hypothesis which regards the regions presenting high contrast in a certain context as salient. Although the contrast hypothesis is valid in many cases, it can’t handle some difficult cases, especially when the salient object is large. To make up for the deficiencies of contrast hypothesis, we incorporate a novel spatial compactness hypothesis which can effectively handle those tough cases. In addition, we propose a unified framework which integrates multiple saliency maps generated on different feature maps built on different hypotheses. Our algorithm can automatically select saliency maps of high quality according to the quality evaluation score defined in this paper. The experimental results demonstrate that each key component of our method contributes to the final performance and the full version of our method outperforms all state-of-the-art methods on the most popular dataset.