Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery

TL;DR

This paper introduces a novel coarse-to-fine low-rank matrix recovery approach for saliency detection that improves the quality of saliency maps by refining initial coarse estimates, especially in complex images with multiple objects.

Contribution

It proposes a new LRMR-based saliency detection model that incorporates a coarse-to-fine framework and a projection learning step for enhanced boundary sharpness and object completeness.

Findings

Outperforms existing LRMR-based methods on benchmark datasets.

Effectively refines coarse saliency maps to produce more accurate results.

Especially effective for images with multiple objects.

Abstract

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.