  Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency
detection by decomposing image features into a low-rank component associated
with background and a sparse component associated with visual salient regions.
Despite its great potential, existing LRMR-based saliency detection methods
seldom consider the inter-relationship among elements within these two
components, thus are prone to generating scattered or incomplete saliency maps.
In this paper, we introduce a novel and efficient LRMR-based saliency detection
model under a coarse-to-fine framework to circumvent this limitation. First, we
roughly measure the saliency of image regions with a baseline LRMR model that
integrates a $\ell_1$-norm sparsity constraint and a Laplacian regularization
smooth term. Given samples from the coarse saliency map, we then learn a
projection that maps image features to refined saliency values, to
significantly sharpen the object boundaries and to preserve the object
entirety. We evaluate our framework against existing LRMR-based methods on
three benchmark datasets. Experimental results validate the superiority of our
method as well as the effectiveness of our suggested coarse-to-fine framework,
especially for images containing multiple objects.
