Salient object detection via cross diffusion-based compactness on multiple graphs

Abstract

The diffusion-based graph has been widely used in saliency detection. Most of the existing methods treat the image boundary patches as background seeds, which may result in the imprecise saliency map if the salient region touches the image boundaries. In this paper, we propose a salient object detection approach via cross diffusion-based compactness on multiple graphs. Firstly, we extract multi-view image features including low-level image features, mid-level image cues (low-level saliency priors), and background-based saliency map. Then, we compute the respective similarity matrix to construct the corresponding graph, and a cross-diffusion algorithm is presented that diffuses each similarity matrix on other’s graphs rather than at itself graph, helping to the compactness-based saliency maps. Additionally, for well propagating the saliency values, we model a propagation mechanism based on cellular automata, by linearly incorporating low-level image features and mid-level image cues together to generate a superior impact factor matrix. Extensive experiment results demonstrate that the proposed method achieves better saliency detection performance against the unsupervised state-of-the-art methods on three public datasets.