Patent ID: 7734087
Filing Date: 2010-06-08
Classification: G06K

Abstract:
1. An apparatus extracting a feature vector, comprising: a data classifier which classifies a training data set into a plurality of subgroups, wherein the subgroups include subgroups that are at least classified, from a global data set of the training data, to each have similar characteristic changes and the subgroups further include at least one subgroup that is classified from a local data set of the training data set, the global data set being obtained from a different source than the local data set; a Principal Component Analysis (PCA) learning unit which performs PCA learning on each of the subgroups to generate a PCA basis vector set of each of the subgroups; a projection unit which projects the PCA basis vector set of each of the subgroups to the training data set; a Linear Discriminant Analysis (LDA) learning unit which performs LDA learning on the training data set resulting from the projection to generate a PCA-based LDA (PCLDA) basis vector set of each of the subgroups; and a feature vector extraction unit which projects the PCLDA basis vector set of each of the subgroups to an input image and extracts a feature vector set of the input image with respect to each of the subgroups.