Cluster-Pairwise Discriminant Analysis

Pattern recognition problems often suffer from the larger intra-class variation due to situation variations such as pose, walking speed, and clothing variations in gait recognition. This paper describes a method of discriminant subspace analysis focused on situation cluster pair. In training phase, both a situation cluster discriminant subspace and class discriminant subspaces for the situation cluster pair by using training samples of non recognition-target classes. In testing phase, given a matching pair of patterns of recognition-target classes, posterior of situation cluster pairs is estimated at first, and then the distance is calculated in the corresponding clusterpairwise class discriminant subspace. The experiments both with simulation data and real data show the effectiveness of the proposed method. Keywords-linear discriminant analysis; cluster;

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Yasushi Makihara, Yasushi Yagi
• Keywords: Class Discriminant Subspace, Computer Vision, Discriminant Subspace, ICPR 2010, Situation Cluster Pairs