Co-saliency detection aims to discover common and salient objects in an image
group containing more than two relevant images. Moreover, depth information has
been demonstrated to be effective for many computer vision tasks. In this
paper, we propose a novel co-saliency detection method for RGBD images based on
hierarchical sparsity reconstruction and energy function refinement. With the
assistance of the intra saliency map, the inter-image correspondence is
formulated as a hierarchical sparsity reconstruction framework. The global
sparsity reconstruction model with a ranking scheme focuses on capturing the
global characteristics among the whole image group through a common foreground
dictionary. The pairwise sparsity reconstruction model aims to explore the
corresponding relationship between pairwise images through a set of pairwise
dictionaries. In order to improve the intra-image smoothness and inter-image
consistency, an energy function refinement model is proposed, which includes
the unary data term, spatial smooth term, and holistic consistency term.
Experiments on two RGBD co-saliency detection benchmarks demonstrate that the
proposed method outperforms the state-of-the-art algorithms both qualitatively
and quantitatively.

本文提出了一种基于分层稀疏重建和能量函数精化的 RGBD 图像联合显著性检测方法，能够较好地提升图像在整体和局部层面的一致性和稀疏性，对于计算机视觉任务有一定的应用价值。