文献类型：期刊文献

英文题名：Cross-domain transfer person re-identification via topology properties preserved local fisher discriminant analysis

作者：Gu, Xiaoqing[1];Ni, Tongguang[1];Wang, Weibo[2];Zhu, Junqing[3]

机构：[1]Changzhou Univ, Sch Informat Sci & Engn, Changzhou 213164, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Case Western Reserve Univ, Case Ctr Imaging Res, Cleveland, OH 44106 USA

年份：2020

外文期刊名：JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING

收录：;EI(收录号：20200207990884);WOS:【SCI-EXPANDED(收录号:WOS:000505384300004)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 61976028 and 61806026, by the Natural Science Foundation of Jiangsu Province under Grant BK 20180956.

语种：英文

外文关键词：Transfer learning; Person re-identification; Topology properties preserve; Local Fisher discriminant analysis

摘要：Person re-identification (Re-ID) systems aim to identify a person appeared in non-overlapping cameras. However, a sufficient amount of pairwise cross-camera-view person images are often not available in a new scenario. Transfer learning can assist the new Re-ID system through leveraging knowledge from other related scenarios. Since the images from existed scenarios may not be the exact representative samples, how to learn a robust transfer Re-ID model with limited labeled person images is still a challenge so far. To solve this problem, a novel cross-domain transfer person Re-ID via topology properties preserved local Fisher discriminant analysis (TPPLFDA) method is proposed in this paper. Making an assumption that all person images in the new and related scenarios share common manifold, TPPLFDA projects all cross-domain images into a low dimensional linear subspace, while preserves the topology properties according to the geodesic distances on manifold. Then, multiple cross-domain datasets as source domains are considered and kernel TPPLFDA for multi-source domain transfer is proposed, so that TPPLFDA can handle more complex cross-domain transfer Re-ID tasks. Extensive experiments on several transfer Re-ID datasets show that TPPLFDA is effective.