Salient Object Detection Via Double Sparse Representations Under Visual Attention Guidance

Abstract

This paper introduces a novel method for salient object detection from the perspective of sparse representation under visual attention guidance. After pretreatment and regional analysis with eye fixation detection and multi scale segmentation, regions that are used to make up the foreground and background dictionaries are respectively selected by sorting the visual attraction level of all image regions. For saliency measurement, the reconstruction errors instead of common local and global contrasts are used as the saliency indicator, which is expected to improve the object integrity. In addition, the multi scale workflow is conductive to enhance the robustness for objects of different sizes. The proposed method was compared to six state-of-the-art saliency detection methods using three benchmark datasets, and it was confirmed to have more favorable performance in the detection of multiple objects as well as maintaining the integrity of the object area.