Visual Salience Learning via Low Rank Matrix Recovery

Li, Junxia; Ding, Jianli; Yang, Jian

doi:10.1007/978-3-319-16811-1_8

Cited by 3 publications

(1 citation statement)

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“…Zou et al [12] introduced segmentation priors to cooperate with sparse saliency in an advanced manner. To preserve the entirety of detection objects, saliency fusion models (e.g., [24,34,35,36]) were proposed thereafter. For instance, double low-rank matrix recovery (DLRMR) was suggested in [24] to fuse saliency maps detected by multiple approaches.…”

Section: Lrmr-based Saliency Detection Methodsmentioning

confidence: 99%

Coarse-to-fine salient object detection with low-rank matrix recovery

Zheng

You

2020

Neurocomputing

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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 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.

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