Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery
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Despite the great potential of using the low-rank matrix recovery (LRMR) theory on the task of salient object detection, existing LRMR-based approaches scarcely consider the interrelationship among elements within the sparse components and suffer from high computational cost. In this paper, we propose a novel LRMR-based saliency detection method under a coarse-to-fine framework to circumvent these two limitations. The first step of our approach is to generate a coarse saliency map by integrating a ℓ_1-norm sparsity constraint imposed on the sparse matrix and a Laplacian regularization for smoothness. Following this, we aim to exploit and reveal the interrelationship among sparse elements and to increase detection recall values near the object boundaries using a learned mapping function to precisely distinguish foreground and background in the cluttered or complex scenes. Extensive experiments on three benchmark datasets demonstrate that our method can achieve enhanced performance compared with other 12 state-of-the-art saliency detection approaches, and also verifies the efficacy of our coarse-to-fine architecture.
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