Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

TL;DR

This paper introduces an edge-preserving, multi-scale neural network for salient object detection that improves boundary clarity and contextual understanding, achieving state-of-the-art results on multiple datasets.

Contribution

It presents a novel end-to-end neural network combining edge preservation and multi-scale context for salient object detection, addressing limitations of existing CNN methods.

Findings

Achieves sharp object boundaries in saliency maps.

Demonstrates superior performance on benchmark datasets.

Applicable to RGB-D saliency detection with depth refinement.

Abstract

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.