Depth-Aware Saliency Detection Using Discriminative Saliency Fusion

In this paper, we propose a multi-stage depth-aware saliency model for salient region detection. We evaluate saliency on different features at low, mid and high levels, by taking account of primary depth and appearance contrasts, different feature weighted factors and location priors, respectively. Unlike most existing depth-aware saliency models that use a linear or experiential fusion formula to combine saliency maps from different features, we calculate saliency of each feature individually at each level and learn a discriminative saliency fusion (DSF) regressor based on random forest to estimate the saliency measures of regions. Both subjective and objective evaluations on two public datasets designed for depth-aware saliency detection demonstrate that the proposed saliency model consistently outperforms the stateof-the-art saliency models on saliency detection performance.