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论文类型：期刊论文

发表时间：2018-03-01

发表刊物：PATTERN RECOGNITION

收录刊物：SCIE、EI、Scopus

卷号：75

期号：,SI

页面范围：63-76

ISSN号：0031-3203

关键字：Person re-identification; Feature transformation; Semantic representation learning; Semantic projection learning

摘要：Feature transformation is of great importance to strengthen the descriptive power of feature representation for many classification and recognition tasks. In this paper, we propose a novel cross-view semantic projection learning method for extracting latent semantics from the hand-crafted features. Specifically, the shared latent basis matrix, the view-specific semantic projection functions and the optimal associations of different views are jointly learned in a unified matrix factorization framework, to get a common semantic space where images of the same person can be well characterized. We further present a generalization of the approach to multiple views. Extensive experiments on a series of challenging datasets highlight the superiorities of the proposed algorithm and demonstrate the effectiveness of the generalized version in multi-view person re-identification applications. (C) 2017 Elsevier Ltd. All rights reserved.