Deep learning framework for saliency object detection based on global prior and local context

Abstract. The saliency object detection is a hot topic of computer vision. Traditional saliency detection methods are overly dependent on handcrafted low-level features. The saliency detection methods based on deep learning can effectively solve the problem, which extracts high-level features automatically. However, there are some noises in the extracted high-level features that affect the detection performance. We propose a deep learning framework for saliency detection based on global prior and local context. First, we use feature maps generated by combining some middle-level features as the input of global-prior-based deep learning model, which can reduce the interference of distracting feature information for the saliency detection. Then, two deep learning models use respectively local contexts of color image and depth map as input, which combine global prior to generate the initial saliency map. Finally, the optimized saliency map can be obtained based on spatial consistence and appearance similarity. Experiments on two publicly available datasets show that the proposed method performs better than other nine state-of-the-art approaches.