Object Categorization Based on Kernel Principal Component Analysis of Visual Words

Many researchers are studying object categorization problem. It is reported that bag of keypoints approach which is based on local features without topological information is effective for object categorization. Conventional bag of keypoints approach selects the visual words by clustering and uses the similarity with each visual word as the features for classification. In this paper, we model the ensemble of visual words, and the similarities with ensemble of visual words not each visual word are used for classification. Kernel principal component analysis (KPCA) is used to model them and extract the information specialized for each category. The projection length in subspace is used as features for support vector machine (SVM). There are two reasons why we use KPCA to model the ensemble of visual words. The first reason is to model the non-linear variations induced by various kinds of visual words. The second reason is that KPCA of local features is robust to pose variations. The proposed method is evaluated using Caltech 101 database. We confirm that the proposed method is comparable with the state of the art methods without absolute position information.