Look, Perceive and Segment: Finding the Salient Objects in Images via Two-Stream Fixation-Semantic CNNs

Abstract

Recently, CNN-based models have achieved remarkable success in image-based salient object detection (SOD). In these models, a key issue is to find a proper network architecture that best fits for the task of SOD. Toward this end, this paper proposes two-stream fixation-semantic CNNs, whose architecture is inspired by the fact that salient objects in complex images can be unambiguously annotated by selecting the pre-segmented semantic objects that receive the highest fixation density in eye-tracking experiments. In the two-stream CNNs, a fixation stream is pre-trained on eye-tracking data whose architecture well fits for the task of fixation prediction, and a semantic stream is pre-trained on images with semantic tags that has a proper architecture for semantic perception. By fusing these two streams into an inception-segmentation module and jointly fine-tuning them on images with manually annotated salient objects, the proposed networks show impressive performance in segmenting salient objects. Experimental results show that our approach outperforms 10 state-of-the-art models (5 deep, 5 non-deep) on 4 datasets.