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Most of the existing works on person re-identification have focused on improving matching rate at top ranks. Few efforts are devoted to address the problem of efficient storage and fast search for person re-identification. In this paper, we investigate the prevailing hashing method, originally designed for large scale image retrieval, for fast person re-identification with efficient storage. We propose...
Existing VMI techniques have high overhead, and require customized introspection programs/tools for different guest OS versions - lack of generality. In this paper, we present Shadow Context, a system for close-to-real time manual-effort-free VMI. Shadow Context can meet several important real-world VMI needs which existing VMI techniques cannot. Compared to other automatic introspection tool generation...
Semi-supervised learning algorithms make use of labeled and unlabeled samples. A large number of experiments show that the use of unlabeled samples may improve approximation power. However, there is seldom quantitative analysis of approximation power when the number of samples increases. In this paper a semi-supervised learning algorithm is constructed based on diffusion matrices. We establish the...
Support vector machines (SVM) can overcome the disadvantage of traditional anomaly detection, which need large sample data and have great effect in real-time detection, but has the disadvantage of slow training velocity. Least squares support vector machines (LS-SVM) can overcome the disadvantage of slow training velocity, but makes the solution lose sparsity and robustness. So a weighted LS-SVM (WLS-SVM)...
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