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13 March 2018 Normalized distance aggregation of discriminative features for person reidentification
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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).
© 2018 SPIE and IS&T 1017-9909/2018/$25.00 © 2018 SPIE and IS&T
Li Hou, Kang Han, Wanggen Wan, Jenq-Neng Hwang, and Haiyan Yao "Normalized distance aggregation of discriminative features for person reidentification," Journal of Electronic Imaging 27(2), 023006 (13 March 2018).
Received: 27 September 2017; Accepted: 19 February 2018; Published: 13 March 2018

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