Sijia Cai

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Discriminative dictionary learning aims to learn a dictionary from training samples to enhance the discriminative capability of their coding vectors. Several discrimination terms have been proposed by assessing the prediction loss (e.g., logistic regression) or class separation criterion (e.g., Fisher discrimination criterion) on the coding vectors. In this(More)
Background modeling is a critical component for various vision-based applications. As the data of these practical problems get larger and larger, most traditional methods tend to be unsatisfactory. In this paper, we propose a Sparse Outliers Iterative Removal (SO-IR) algorithm for the large-scale stable background modeling problems. The proposed algorithm(More)
—Background modeling is a critical component for various vision-based applications. Most traditional methods tend to be inefficient when solving large-scale problems. In this paper, we introduce sparse representation into the task of large-scale stable-background modeling, and reduce the video size by exploring its " discriminative " frames. A cyclic(More)
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