Semi-supervised person re-identification using multi-view clustering

Highlights：

• We design a semi-supervised feature representation framework for person Re-Identification which effectively utilizes both labeled and unlabeled training data to learn a discriminative representation so that person images across disjoint camera views can be reliably matched.

• A multi-view clustering method is proposed to integrate features from multiple Convolutional Neural Networks for clustering, which can give more accurate label estimation for unlabeled data.

• Each of our Convolutional Neural Networks utilizes a siamese network that simultaneously computes the identification loss and verification loss, which simultaneously learns a discriminative Convolutional Neural Network embedding and a similarity metric, and thus improving pedestrian retrieval accuracy. Extensive experiments on large-scale person Re-Id datasets demonstrate the effectiveness of our method.