Salient object detection via multi-grained refinement polygon topology positive feedback

Abstract

Despite significant progress in current salient object detection (SOD) tasks, they are limited by overlapping regions, broken connections, voids and neglecting explicit exploration of background regions. In this paper, we propose a multi-grained refinement polygonal topology network (MPT) that explores two important components of background mining information and the impact of topological segmentation errors. (1) Unlike existing models that focus on inter-image relationships, we introduce a multi-grained refinement module to discriminate between saliency objects and background information, and introduce inter-image relationships into the pixel-by-pixel segmentation features to enhance the discrimination of segmentation features, and this Multi-grained refinement is the basis for mining saliency objects. (2) Based on the multi-grained refinement features, we introduce a polygonal topology-aware module and add topological constraints as penalty terms to regularize the focus loss to effectively resolve topological errors to enhance the quality of the performance of salient object detection. Meanwhile, we introduce a measurements-based on positive feedback model to integrate the decision fusion of multi-model perception results to obtain higher accuracy of saliency prediction while ensuring that the prediction results will not deteriorate. Extensive experiments on six benchmark and extended datasets demonstrate that our proposed MPTNet exhibits excellent performance both qualitatively and quantitatively and outperforms state-of-the-art saliency detectors.