TY - GEN
T1 - Saliency detection via diversity-induced multi-view matrix decomposition
AU - Sun, Xiaoli
AU - He, Zhixiang
AU - Zhang, Xiujun
AU - Zou, Wenbin
AU - Baciu, George
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85016011820&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54181-5_9
DO - 10.1007/978-3-319-54181-5_9
M3 - Conference article published in proceeding or book
SN - 9783319541808
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 151
BT - Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
PB - Springer Verlag
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
Y2 - 20 November 2016 through 24 November 2016
ER -