Person Re-identification via Attribute Confidence and Saliency

Person re-identification is a problem of recognising and associating persons across different cameras. Existing methods usually take visual appearance features to address this issue, while the visual descriptions are sensitive to the environment variation. Relatively, the semantic attributes are more robust in complicated environments. Therefore, several attribute-based methods are introduced, but most of them ignored the diversities of different attributes. We epitomize the diversities of different attributes as two folds: the attribute confidence which denotes the descriptive power, and the attribute saliency which expresses the discriminative power. Specifically, the attribute confidence is determined by the performance of each attribute classifier, and the attribute saliency is defined by their occurrence frequency, similar to the IDF (Inverse Document Frequency) [1] idea in information retrieval. Then, each attribute is assigned an appropriate weighting according to its saliency and confidence when calculating similarity distances. Based on above considerations, a novel person re-identification method is proposed. Experiments conducted on two benchmark datasets have validated the effectiveness of the proposed method.