Saliency prediction based on new deep multi-layer convolution neural network

Recent advances in saliency detection have utilized deep learning to obtain high-level features to detect salient regions. These advances have demonstrated superior results over previous works that utilize hand-crafted low-level features for saliency detection. In this paper, we propose a new multilayer Convolutional Neural Network (CNN) model to learn high-level features for saliency detection. Compared to other methods, our method presents two merits. First, when performing features extraction, apart from the convolution and pooling step in our method, we add Restricted Boltzmann Machine (RBM) into the CNN framework to obtain more accurate features in intermediate step. Second, in order to deal with case of non-linear classification, we add the Deep Belief Network (DBN) classifier at the end of this model to classify the salient and non-salient regions. Quantitative and qualitative experiments on three benchmark datasets demonstrate that our method performs favorably against the state-of-the-art methods.