Coarse-to-fine salient object detection based on deep convolutional neural networks

With explosive growth of image data, automatic image interpretation becomes more and more important. Saliency detection is one of the fundamental problems. To predict the saliency map, traditional saliency detection approaches use handcrafted features, which are not robust for complex scene. Recently, convolutional neural network (CNN) have shown good performance in computer vision problems. In this paper, we propose a coarse-to-fine approach combining pixel-wise FCN with superpixel-based CNN for detecting salient objects with precise boundaries. Firstly, the fully convolutional network (FCN) model is used to produce a coarse saliency map. Instead of patch-based CNN taking in overlapping patches as samples, the FCN model adopts the pixel-wise structure which can predict the location of the salient objects from the global aspect. Then, superpixel clustering is presented to decompose the image into homogeneous superpixels. For each superpixel, the local superpixel-based CNN model is created to integrate the coarse saliency map with the original image information for refining the detected salient objects with precise boundaries. Experimental results on large benchmark databases demonstrate the proposed method perform well when tested against the state-of-the-art methods.