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Recent advances in saliency detection have utilized deep learning to obtainhigh level features to detect salient regions in a scene. These advances havedemonstrated superior results over previous works that utilize hand-crafted lowlevel features for saliency detection. In this paper, we demonstrate thathand-crafted features can provide complementary information to enhanceperformance of saliency detection that utilizes only high level features. Ourmethod utilizes both high level and low level features for saliency detectionunder a unified deep learning framework. The high level features are extractedusing the VGG-net, and the low level features are compared with other parts ofan image to form a low level distance map. The low level distance map is thenencoded using a convolutional neural network(CNN) with multiple 1X1convolutional and ReLU layers. We concatenate the encoded low level distancemap and the high level features, and connect them to a fully connected neuralnetwork classifier to evaluate the saliency of a query region. Our experimentsshow that our method can further improve the performance of state-of-the-artdeep learning-based saliency detection methods.

Deep Saliency with Encoded Low level Distance Map and High Level Features