View-specific subspace learning and re-ranking for semi-supervised person re-identification

Abstract

Person re-identification (re-ID) focuses on matching the same person across non-overlapping camera views. Most existing methods require tedious manual annotation and can only learn a unitary transformation for images across views, which severely lack of scalability and suffer from view-specific biases. To address these issues, we put forward a View-Specific Semi-supervised Subspace Learning (VS-SSL) approach that can learn specific projections for each view, utilizing limited labeled data to guide the training while leveraging abundant unlabeled data simultaneously. Moreover, a novel re-ranking strategy is proposed to boost the performance further, which re-estimates the similarity between probe and galleries according to the overlap ratio between their expanded neighbors and their position in each other’s ranking list. The effectiveness of the proposed framework is evaluated on several widely-used datasets (VIPeR, PRID450S, PRID2011, CUHK01 and Market-1501), yielding superior performance for both semi-supervised and supervised re-ID.