Salient object detection via color and texture cues

In this paper, we present a new bottom-up salient object detection approach by constructing two graphs using color and texture features within the manifold ranking framework. First, we calculate the saliency of boundary patches and exclude the ones with high saliency which might be a part of saliency object. Second, we adopt a two-stage scheme for salient detection via affinity propagation clustering and graph-based manifold ranking. The background-based saliency detection aims to obtain the salient object regions as much as possible. In the foreground-based saliency detection, a similar computation is processed as that in the former step and yet slightly different. Instead of simultaneously using all the extracted boundary patches or foreground patches as queries, we compute saliency by using the patches in each cluster in turn and integrating them. At last, the final saliency map is generated by linearly combining two saliency maps respectively exploring color and texture cues. Both qualitative and quantitative evaluations on three publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 21 state-of-the-art methods.