Semi-supervised Component Analysis

Object re-identification techniques are essential to improve the identification performance in video surveillance tasks. The re-identification problem is equal to a multi-view problem that an unknown individual is identified across spatially disjoint data. For the re-identification techniques, several multi-view feature transformation methods have been proposed. These methods are formulated by the supervised learning framework and show the better performances in multi-view classification tasks in which the training data are observed by the different sensors. However, in the reidentification tasks, these methods may not be required because the simple feature transformation method such as linear discriminant analysis (LDA) shows the reasonable identification rates. In this paper, we propose a novel semi supervised feature transformation method, which is formulated as a natural coupling with PCA and LDA modeled by the graph embedding framework. Our method showed best re-identification performances compared with other feature transformation methods.