Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68389
Title: Saliency detection via diversity-induced multi-view matrix decomposition
Authors: Sun, XL
He, ZX
Zhang, XJ
Zou, WB
Baciu, G 
Issue Date: 2016
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2016, v. 10111, p. 137-151 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In this paper, a diversity-induced multi-view matrix decomposition model (DMMD) for salient object detection is proposed. In order to make the background cleaner, Schatten-p norm with an appropriate value of p in (0,1] is used to constrain the background part. A group sparsity induced norm is imposed on the foreground (salient part) to describe potential spatial relationships of patches. And most importantly, a diversity-induced multi-view regularization based Hilbert-Schmidt Independence Criterion (HSIC), is employed to explore the complementary information of different features. The independence between the multiple features will be enhanced. The optimization problem can be solved through an augmented Lagrange multipliers method. Finally, high-level priors are merged to boom the salient regions detection. Experiments on the widely used MSRA-5000 dataset show that the DMMD model outperforms other state-of-the-art methods.
Description: Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016
URI: http://hdl.handle.net/10397/68389
ISBN: 978-3-319-54180-8 (print)
978-3-319-54181-5 (online)
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-54181-5_9
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