Set-based classification for person re-identification utilizing mutual-information

Identifying individuals in multi-view camera network, known as person re-identification, becomes an emerging topic for video surveillance. In this paper, we address person re-identification as a set-based classification problem and introduce mutual-information to fully utilize gallery information. Firstly, we define a set-based structure that contains pairwise features between query image and gallery images. Then these features are fed into a set-class model, which exploits the relationship between set and class label (person identity) using mutual-information. Finally, we estimate and rank the mutual-information scores, and the corresponding label of the highest score is assigned to the query image. Our method has gained a superior performance compared with the state-of-the-art in the benchmark datasets i-LIDS and ETHZ.