Person Re-Identification by Optimally Organizing Multiple Similarity Measures

Person re-identification refers to matching people across disjoint camera views. Most existing person re-identification methods use the same feature descriptors and similarity metrics for all pedestrian pairs. However, these methods ignore that image pairs with different visual consistency conditions are sensitive to different features and metrics. In this paper, we propose to optimally organize multiple similarity measures of global pedestrian and body part pairs with respect to different visual consistency measures (VCM). First, we compute multiple similarity measures for global image and body parts. Then, we group the global image and body part set into three classes based on their VCM value, respectively. Finally, the VCM-specific similarity measures of pedestrian as well as body part pairs are selected and optimally organized to form an ensemble by the reliability estimation and adaptively weighting combination. This method is termed as multiple similarity ensemble based on the visual consistency measure (MSE-VCM). Our contributions are 1) the visual consistency measure method which can select the most appropriate similarity measures for image pairs and 2) optimal organization of these VCM-specific features and metrics on global image and body parts. Extensive experiments on three challenging data sets are conducted. Results demonstrate that our method achieves the comparable performance versus the state-of-the-art methods.