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The theory of sparse and low-rank representation has worked competitive performance in the field of salient object detection. Generally, the salient object is represented as sparse error while the non-salient region is constrained by the property of low-rank. However, sparsity ignores the global structure which may break up the low-rank property. Besides,(More)
REN Wei-Ya, LI Shuo-Hao, Guo Qiang; LI Guo-Hui; Zhang Jun Email: weiyren.phd@gmail.com; lishuohao08@126.com; guoqiang05@nudt.edu.cn; gli2010a@163.com; zhangjun1975@nudt.edu.cn. (College of Information System and Management, National University of Defense Technology, Hunan Changsha, 410073, China) Abstract: The key in agglomerative clustering is to define(More)
We address the person re-identification problem by efficient data representation method. Based on the Relaxed Nonnegative matrix factorization (rNMF) which has no sign constraints on the data matrix and the basis matrix, we consider two regularizations to improve the Relaxed NMF, which are the local manifold assumption and a rank constraint. The local(More)
In this paper, we propose a method to detect abnormal events using a novel unsupervised kernel learning algorithm. The key of our method is to learn a suitable feature space and the associated kernel function of the training samples. By considering the self-similarity property of training samples, we assume that the training samples will show the distinctly(More)
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