Abstract:At present, in the field of person reidentification (re-id), the commonly used supervised learning algorithms require a large amount of labeled samples, which is not conducive to the model promotion. On the other hand, the accuracy of unsupervised learning algorithms is lower than supervised algorithms due to the lack of discriminant information. To address these issues, we make use of a small amount of labeled samples to add discriminant information in the basic dictionary learning. Moreover, the sparse coeff… Show more
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