LAUSR

Abstract The graph model is a reliable propagation mechanism in saliency detection, saliency value propagation diffusion results between any two nodes are determined merely by defining an effect affinity matrix. Most existing methods generally calculamatrix using mean values of single or multiple feature vectors, not fully exploit the diversity and consistency of multi-view features, may produce poor foreground uniformity and completeness in the complex scene. Multi-views should share an affinity matrix as well as complement each other. In this paper, we propose a graph-based saliency detection with a learning joint affinity matrix. First, we capture multiple appearance features from the image and generate a learning joint affinity matrix based on low-rank representation. Then, for computing an effect affinity matrix, we linearly integrate the traditional affinity matrix and learning joint affinity matrix, helping to construct an affinity graph for diffusion-based compactness. Finally, to effectively optimize the initial saliency map, we diffuse the learning joint affinity matrix and traditional impact factor matrix via cross-view diffusion processing, which begets an approving advantage for single-layer cellular automata. Results on three benchmark datasets demonstrate that our proposed method shows the best performance against nine state-of-the-art models.