Salient Object Detection with Edge Recalibration

Abstract

Salient Object Detection (SOD) based on Convolutional Neural Networks (CNNs) has been widely studied recently. How to maintain a complete and clear object boundary structure is still a key issue. Existing works with the utilization of edge information have already improved this issue to some extent. However, these methods extract boundary features indiscriminately, which may weaken useful edge information and mislead edge construction. To address this problem, we present an Edge Recalibration Network (ERN) model for image-based SOD to perform edge-guided features effectively. In a specific, a progressive Fully Convolutional neural Networks (FCNs) for SOD is adopted to incorporate multi-scale and multi-level features. Besides, to locate the edge position and preserve the boundary features accurately, we propose an edge enhancement module with pixel-wise semantic-edge integration and channel-wise feature recalibration. Based on pixelwise semantic-edge integration, the semantic features and boundary features are integrated into the holistic feature maps. Based on channel-wise feature recalibration, the boundary features selectively recalibrate salient semantic features on channel dimension, aiming to enhance useful features and suppress useless features, for the similarity of boundary features and salient semantic features. Experimental results on five popular benchmark datasets show that the proposed model ERN outperforms other state-of-the-art methods under different evaluation metrics.