Salient object detection employing a local tree-structured low-rank representation and foreground consistency. (August 2019)

• Record Type: Journal Article
• Authors: Zhang, Qiang; Huo, Zhen; Liu, Yi; Pan, Yunhui; Shan, Caifeng; Han, Jungong
• Is Part Of: Pattern recognition. Volume 92(2019:Aug.)
• Page Start: 119
• Page End: 134
• Publication Date: 2019-08
• Subjects: Salient object detection -- Structured low-rank representation -- Background dictionary -- Foreground consistency; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.03.023
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A local tree-structured low-rank representation (TS-LRR) model for salient object detection is introduced. The foreground consistency within potential foreground regions is considered. A primitive background dictionary is constructed for the proposed model. Experimental results on public datasets demonstrate the effectiveness of the proposed model over the state-of-the-art methods. Abstract: We propose a local tree-structured low-rank representation (TS-LRR) model to detect salient objects under the complicated background with diverse local regions, which is problematic for most low-rank matrix recovery (LRMR) based salient object detection methods. We first impose a local tree-structured low-rank constraint on the representation coefficients matrix to capture the complicated background. Specifically, a primitive background dictionary is constructed for TS-LRR to promote its background representation ability, and thus enlarge the gap between the salient objects and the background. We then impose a group-sparsity constraint on the sparse error matrix with the intention to ensure the saliency consistency among patches with similar features. At last, a foreground consistency is introduced to identically highlight the distinctive regions within the salient object. Experimental results on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model over the state-of-the-art methods.