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While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount...
Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessary in many visual systems. To that end, matching RGB images with infrared images...
At present, most fault diagnosis for grinding system is based on artificial judgments, which is inefficient, low accurate, high cost and easy to cause casualties. The traditional neural network has an unsatisfying performance to predict on high dimensional dataset, and is hard to extract crucial features, which brings about terrible classification results. To solve the above problems, the paper present...
The performance of popular and classical k-nearest neighbor classifier depends on the distance metric. Large margin nearest neighbor classifier using gradient optimization method is prone to local minima. In this paper, we present a Mahalanobis metric learning method based on cutting plane algorithm which reduces largely constraints for solving the semidefinite programming problem. Experimental results...
In this paper, the material measure of BBD ball mill based on multi-information data fusion is researched. By combining the Rough Set and Radial Basis Function neural network, this method can not only solve the priori difficulty in obtaining information in data fusion and a large number of redundant data existing problems in system, but also greatly increase the approximation ability and learning...
With an aging underground long-distance oil/gas pipeline, ever-encroaching population and increasing oil price, the burden on pipeline agencies to efficiently prioritize and maintain the rapidly deteriorating underground utilities is increasing. Failure rate prediction is the most important part of risk assessment, and the veracity of the failure rate impacts the rationality and applicability of the...
Distinct from conventional techniques where the neural network (NN) is employed to solve the problem of paper currency verification, in this paper, we shall present a novel method by applying the support vector machine (SVM) approach to distinguish counterfeit banknotes from genuine ones. On the basis of the statistical learning theory, SVM has better generalization ability and higher performance...
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