Multi-feature fusion based re-ranking for person re-identification

摘要

In computer vision applications such as person re-identification the optimization of rank list is an important issue. In order to address this issue, a multi-feature fusion based re-ranking technique is proposed. In most of the conventional methods, a long feature vector is formulated from a single modality. Whereas, in the proposed approach, multiple features from the image are extracted and combined into a unified/hybrid vector. Later a joint feature vector is presented after fusion. The Mahalanobis distance is calculated for checking the similarity between the image pairs. A tree based re-ranking algorithm is also proposed that used this combined feature vector and the distance metric. Therefore, by effective use of each feature type, better re-rank can be achieved. We assessed the proposed method on publically available datasets VIPeR and ETHZ. Experimental results demonstrate that the presented approach performs well than exploiting each individual feature.