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Salient object detection increasingly receives attention as an importantcomponent or step in several pattern recognition and image processing tasks.Although a variety of powerful saliency models have been intensively proposed,they usually involve heavy feature (or model) engineering based on priors (orassumptions) about the properties of objects and backgrounds. Inspired by theeffectiveness of recently developed feature learning, we provide a novel DeepImage Saliency Computing (DISC) framework for fine-grained image saliencycomputing. In particular, we model the image saliency from both the coarse- andfine-level observations, and utilize the deep convolutional neural network(CNN) to learn the saliency representation in a progressive manner.Specifically, our saliency model is built upon two stacked CNNs. The first CNNgenerates a coarse-level saliency map by taking the overall image as the input,roughly identifying saliency regions in the global context. Furthermore, weintegrate superpixel-based local context information in the first CNN to refinethe coarse-level saliency map. Guided by the coarse saliency map, the secondCNN focuses on the local context to produce fine-grained and accurate saliencymap while preserving object details. For a testing image, the two CNNscollaboratively conduct the saliency computing in one shot. Our DISC frameworkis capable of uniformly highlighting the objects-of-interest from complexbackground while preserving well object details. Extensive experiments onseveral standard benchmarks suggest that DISC outperforms otherstate-of-the-art methods and it also generalizes well across datasets withoutadditional training. The executable version of DISC is available online:http://vision.sysu.edu.cn/projects/DISC.

DISC: Deep Image Saliency Computing via Progressive Representation Learning