Semi-supervised person re-identification using multi-view clustering. (April 2019) - Xin, Xiaomeng; Wang, Jinjun; Xie, Ruji; Zhou, Sanping; Huang, Wenli; Zheng, Nanning
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. Abstract: Person Re-Identification (Re-Id) is a challenging task focusing on identifying the same person among disjoint camera views. A number of deep learning algorithms have been reported for this task in fully-supervised fashion which requires a large amount of labeled training data, while obtaining high quality labels for Re-Id is extremely time consuming. To address this problem, we propose a semi-supervised Re-Id framework by using only a small portion of labeled data and some additional unlabeled samples. This paper approaches the problem by constructing a set of heterogeneous Convolutional Neural Networks (CNNs) fine-tuned using the labeled portion, and then propagating the labels to the unlabeled portion for further fine-tuning the overall system. In this work, label estimation is a key component during the propagation process. We propose a novel multi-view clustering method, which integrates features of multiple heterogeneous CNNs to cluster and generate pseudo labels for unlabeled samples. Then we fine-tune each of the multiple heterogeneous CNNs by minimizing an identification loss and a verification loss simultaneously, using training data with both true labels and pseudo labels. The procedure is iterated until the estimation of pseudo labels no longer changes. Extensive experiments on three large-scale person Re-Id datasets demonstrate the effectiveness of the proposed method.