Evaluating Feature Importance for Re-identification

Person re-identification methods seek robust person matching through combining feature types. Often, these features are assigned implicitly with a single vector of global weights, which are assumed to be universally and equally good for matching all individuals, independent to their different appearances. In this study, we present a comprehensive comparison and evaluation of up-to-date imagery features for person re-identification. We show that certain features play more important roles than others for different people. To that end, we introduce an unsupervised approach to learning a bottom-up measurement of feature importance. This is achieved through first automatically grouping individuals with similar appearance characteristics into different prototypes/clusters. Different features extracted from different individuals are then automatically weighted adaptively driven by their inherent appearance characteristics defined by the associated prototype. We show comparative evaluation on the re-identification effectiveness of the proposed prototype-sensitive feature importance based method as compared to two generic weight based global feature importance methods. We conclude by showing that their combination is able to yield more accurate person re-identification. Chunxiao Liu Tsinghua University, China. e-mail: [email protected] Shaogang Gong Queen Mary University of London, UK. e-mail: [email protected] Chen Change Loy The Chinese University of Hong Kong, Hong Kong. e-mail: [email protected] Xinggang Lin Tsinghua University, China. e-mail: [email protected]