Adaptive Irregular Graph Construction-Based Salient Object Detection

Saliency detection represents a vital pre-processing stage of computer vision. Most existing propagation-based salient object detection methods construct a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-regular graph for saliency propagation. Applying a regular graph to a vast smooth region is potentially prone to unnecessary or prolonged propagation errors, leading to the excessive highlighting of the background regions. To mitigate such problems, we substitute the conventional <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-regular graph with an adaptive irregular graph for saliency value propagation, thereby avoiding unnecessary iterations over a vast smooth region. We first perform a clustering analysis based on the smoothness, color, and other features of regions. The new graph boosts an adaptive link density by considering the clustering result. In addition, we propose a seeding strategy for the propagation. Based on our experimental studies of six major benchmark datasets, our method performed favorably against the other state-of-the-art methods, both quantitatively and qualitatively.