Person re-identification: what features are important?

Authors:
Chunxiao Liu;Shaogang Gong;Chen Change Loy;Xinggang Lin
Affiliations:
Dept. of Electronic Engineering, Tsinghua University, China;School of EECS, Queen Mary University of London, UK;Vision Semantics Ltd., UK;Dept. of Electronic Engineering, Tsinghua University, China
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Year:
2012

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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.