Salient Object Detection via Weighted Low Rank Matrix Recovery

Image-based salient object detection is a useful and important technique, which can promote the efficiency of several applications such as object detection, image classification/retrieval, object co-segmentation, and content-based image editing. In this letter, we present a novel weighted low-rank m...

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Bibliographic Details
Published in:IEEE signal processing letters 2017-04, Vol.24 (4), p.490-494
Main Authors: Tang, Chang, Wang, Pichao, Zhang, Changqing, Li, Wanqing
Format: Article
Language:English
Subjects:
Background prior
Convergence
Feature extraction
Image color analysis
Matrix decomposition
Object detection
saliency object detection
Signal processing algorithms
Sparse matrices
weighted low-rank matrix recovery (WLRR)
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Summary:Image-based salient object detection is a useful and important technique, which can promote the efficiency of several applications such as object detection, image classification/retrieval, object co-segmentation, and content-based image editing. In this letter, we present a novel weighted low-rank matrix recovery (WLRR) model for salient object detection. In order to facilitate efficient salient objects-background separation, a high-level background prior map is estimated by employing the property of the color, location, and boundary connectivity, and then this prior map is ensembled into a weighting matrix which indicates the likelihood that each image region belongs to the background. The final salient object detection task is formulated as the WLRR model with the weighting matrix. Both quantitative and qualitative experimental results on three challenging datasets show competitive results as compared with 24 state-of-the-art methods.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2016.2620162