On-the-fly feature importance mining for person re-identification

State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with generic weights, which are assumed to be universally and equally good for all individuals, independent of people's different appearances. In this study, we show that certain features play more important role than others under different viewing conditions. To explore this characteristic, we propose a novel unsupervised approach to bottom-up feature importance mining on-the-fly specific to each re-identification probe target image, so features extracted from different individuals are weighted adaptively driven by their salient and inherent appearance attributes. Extensive experiments on three public datasets give insights on how feature importance can vary depending on both the viewing condition and specific person's appearance, and demonstrate that unsupervised bottom-up feature importance mining specific to each probe image can facilitate more accurate re-identification especially when it is combined with generic universal weights obtained using existing distance metric learning methods.