Multi-level and multi-scale deep saliency network for salient object detection

Abstract Traditional saliency model usually utilize handcrafted image features and various prior knowledge to pop out salient regions from complex surroundings. In this paper, we propose a novel FCN-like deep convolutional neural network for pixel-wise salient object detection. Our deep network automatically learns multi-level feature from different convolutional layers of a pre-trained convolutional neural network. Moreover, deeper side outputs are connected to the shallower ones, which provides a better feature representation and helps shallow side outputs to accurately locate salient regions. In addition, we adopt a weighted-fusion module to combine different side outputs for utilizing multi-scale and multi-level features. Finally, a fully connected CRF model can be optimally incorporated to improve spatial coherence and contour localization in the fused saliency map. Both qualitative and quantitative evaluations on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 17 state-of-the-art methods.