Object-level saliency detection based on spatial compactness assumption

Object-level saliency detection is an important aspect of visual saliency. Most existing methods build on the contrast assumption. It tends to highlight the saliency of the regions with high contrast in a certain context, but it does not work well in some scenarios. In this paper, we propose a novel spatial compactness assumption which considers that salient regions are spatially more compact than background regions. Based on it, we present two object-level saliency detection methods: the patch-based method and the region-based method. In the experiments, both methods are compared with nine state-of-the-art methods on a public dataset and the best performances are obtained. The experimental results show that the spatial compactness assumption is valid and the proposed methods can uniformly highlight salient objects, even for large ones.