Saliency Detection by Multitask Sparsity Pursuit

Authors:
Congyan Lang;Guangcan Liu;Jian Yu;Shuicheng Yan
Affiliations:
Department of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;Department of Electrical and Computer Engineering, National University of Singapore, Singapore,;Department of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;Department of Electrical and Computer Engineering, National University of Singapore, Singapore,
Venue:
IEEE Transactions on Image Processing
Year:
2012

Citing 0
Cited 7

Memorable basis: towards human-centralized sparse representation

Proceedings of the 20th ACM international conference on Multimedia
Depth matters: influence of depth cues on visual saliency

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Robust image annotation via simultaneous feature and sample outlier pursuit

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Low-rank representation with local constraint for graph construction

Neurocomputing
Superpixel based color contrast and color distribution driven salient object detection

Image Communication
Tag-Saliency: Combining bottom-up and top-down information for saliency detection

Computer Vision and Image Understanding
Ensemble dictionary learning for saliency detection

Image and Vision Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper addresses the problem of detecting salient areas within natural images. We shall mainly study the problem under unsupervised setting, i.e., saliency detection without learning from labeled images. A solution of multitask sparsity pursuit is proposed to integrate multiple types of features for detecting saliency collaboratively. Given an image described by multiple features, its saliency map is inferred by seeking the consistently sparse elements from the joint decompositions of multiple-feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a constrained nuclear norm and as an $\ell_{2, 1}$-norm minimization problem, which is convex and can be solved efficiently with an augmented Lagrange multiplier method. Compared with previous methods, which usually make use of multiple features by combining the saliency maps obtained from individual features, the proposed method seamlessly integrates multiple features to produce jointly the saliency map with a single inference step and thus produces more accurate and reliable results. In addition to the unsupervised setting, the proposed method can be also generalized to incorporate the top-down priors obtained from supervised environment. Extensive experiments well validate its superiority over other state-of-the-art methods.