A Shape-Based Approach for Salient Object Detection Using Deep Learning

Jongpil Kim, Vladimir Pavlovic

Abstract: Salient object detection is a key step in many image analysis tasks as it not only identifies relevant parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. In this paper, we propose a novel salient object detection method that combines a shape prediction driven by a convolutional neural network with the mid and low-region preserving image information. Our model learns a shape of a salient object using a CNN model for a target region and estimates the full but coarse saliency map of the target image. The map is then refined using image specific low-to-mid level information. Experimental results show that the proposed method outperforms previous state-of-the-arts methods in salient object detection.