Estimation of Object Position Based on Color and Shape Contextual Information
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Scene classification based on local autocorrelation of similarities with subspaces
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An approach to the compact and efficient visual codebook based on SIFT descriptor
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
PMA: Pixel-based multi-anchor algorithm for image recognition on multi-core systems
Proceedings of the 2012 International Workshop on Programming Models and Applications for Multicores and Manycores
A segmentation-free method for image classification based on pixel-wise matching
Journal of Computer and System Sciences
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In recent years, 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.