Exploiting Gaussian mixture importance for person re-identification

Person re-identification (ReID) stands for the task of determining the co-occurrence of individuals across a network of cameras with disjoint viewfields. The relevant literature documents a plausible number of contributions so far. KISS metric learning is an effective ReID method. However, as reported in the existing works, KISS metric learning is sensitive to the feature dimensionality and can not capture the multi modes in the dataset. To this end, we propose in this paper a Gaussian Mixture Importance Estimation (GMIE) approach for ReID, which exploits the Gaussian Mixture Models (GMMs) to estimate the observed commonalities of similar and dissimilar person pairs in the feature space. Experiments on three benchmark datasets reveal that our method offers the property of maintaining its efficiency on high-dimensional features. Moreover, the proposed GMIE scores plausible ReID rates as compared to other works.

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