Normalized distance aggregation of discriminative features for person reidentification

作者：Hou, Li^*; Han, Kang; Wan, Wanggen; Hwang, Jenq-Neng; Yao, Haiyan

来源：Journal of Electronic Imaging, 2018, 27(2): 023006.

DOI：10.1117/1.JEI.27.2.023006

• person reidentification
• discriminative features
• discriminant distance metric learning
• distance aggregation
• min-max normalization

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).

• 出版日期2018-3
• 单位黄山学院; 上海大学