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• W2794367943 abstract "We propose an effective person reidentification method based on normalized distance aggregation of discriminative features. Our framework is built on the integration of three high-performance discriminative feature extraction models, including local maximal occurrence (LOMO), feature fusion net (FFN), and a concatenation of LOMO and FFN called LOMO–FFN, through two fast and discriminant metric learning models, i.e., cross-view quadratic discriminant analysis (XQDA) and large-scale similarity learning (LSSL). More specifically, we first represent all the cross-view person images using LOMO, FFN, and LOMO–FFN, respectively, and then apply each extracted feature representation to train XQDA and LSSL, respectively, to obtain the optimized individual cross-view distance metric. Finally, the cross-view person matching is computed as the sum of the optimized individual cross-view distance metric through the min–max normalization. Experimental results have shown the effectiveness of the proposed algorithm on three challenging datasets (VIPeR, PRID450s, and CUHK01)." @default.
• W2794367943 created "2018-03-29" @default.
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• W2794367943 date "2018-03-13" @default.
• W2794367943 modified "2023-09-26" @default.
• W2794367943 title "Normalized distance aggregation of discriminative features for person reidentification" @default.
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• W2794367943 doi "https://doi.org/10.1117/1.jei.27.2.023006" @default.
• W2794367943 hasPublicationYear "2018" @default.
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