Person Re-identification: What Features Are Important?

Chunxiao Liu¹, Shaogang Gong², Chen Change Loy³, and Xinggang Lin¹

¹Dept. of Electronic Engineering, Tsinghua University, China

²School of EECS, Queen Mary University of London, UK

³Vision Semantics Ltd., UK

Abstract. State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods.

LNCS 7583, p. 391 ff.

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