Normalized distance aggregation of discriminative features for person reidentification

Li Hou; Kang Han; Wanggen Wan; Jenq-Neng Hwang; Haiyan Yao

doi:10.1117/1.JEI.27.2.023006

13 March 2018 Normalized distance aggregation of discriminative features for person reidentification

Li Hou, Kang Han, Wanggen Wan, Jenq-Neng Hwang, Haiyan Yao

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 2, 023006 (March 2018). https://doi.org/10.1117/1.JEI.27.2.023006

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).

Citation Download Citation

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). https://doi.org/10.1117/1.JEI.27.2.023006

Received: 27 September 2017; Accepted: 19 February 2018; Published: 13 March 2018

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