Salient Object Detection via Low-Rank and Structured Sparse Matrix Decomposition

Salient object detection provides an alternative solution to various image semantic understanding tasks such as object recognition, adaptive compression and image retrieval. Recently, low-rank matrix recovery (LR) theory has been introduced into saliency detection, and achieves impressed results. However, the existing LR-based models neglect the underlying structure of images, and inevitably degrade the associated performance. In this paper, we propose a Low-rank and Structured sparse Matrix Decomposition (LSMD) model for salient object detection. In the model, a tree-structured sparsity-inducing norm regularization is firstly introduced to provide a hierarchical description of the image structure to ensure the completeness of the extracted salient object. The similarity of saliency values within the salient object is then guaranteed by the `∞-norm. Finally, high-level priors are integrated to guide the matrix decomposition and enhance the saliency detection. Experimental results on the largest public benchmark database show that our model outperforms existing LRbased approaches and other state-of-the-art methods, which verifies the effectiveness and robustness of the structure cues in our model.

Authors

• Houwen Peng
• Bing Li
• Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China
• Weiming Hu State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences School of Information Science and Technology, ShanghaiTech University
• Weihua Xiong Chinese Academy of Sciences
• Congyan Lang Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education), School of Computer and Information Technology, Beijing Jiaotong University

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