Salient Object Detection with Chained Multi-Scale Fully Convolutional Network
Youbao Tang, Xiangqian Wu · 2017
In this paper, we proposed a novel method for effective salient object detection by designing a chained multi-scale fully convolutional network (CMSFCN). CMSFCN contained multiple single-scale fully convolutional networks (SSFCNs), which were integrated successively by using chained connections and generated saliency prediction results from coarse to fine. The chained connections not only combined the saliency prediction result from previous SSFCN with the input image of current SSFCN, but also combined the intermediate features from previous SSFCN and current SSFCN. With these chained connections, the sequential SSFCNs in CMSFCN automatically learned complemental and discriminative features to improve the saliency predictions progressively. Therefore, after jointly training CMSFCN with an end-to-end manner, precise saliency prediction results were produced under a coarse-to-fine behaviour. Compared with seven state-of-the-art CNN based salient object detection approaches over five benchmark datasets, experimental results demonstrated the efficiency and effectiveness of CMSFCN.