Image-set based Classification using Multiple Pseudo-whitened Mutual Subspace Method

Osamu Yamaguchi, Osamu Yamaguchi, Kazuhiro Fukui

2022

Abstract

This paper proposes a new image-set-based classification method, called Multiple Pseudo-Whitened Mutual Subspace Method (MPWMSM), constructed under multiple pseudo-whitening. Further, it proposes to combine this method with Convolutional Neural Network (CNN) features to perform higher discriminative performance. MPWMSM is a type of subspace representation-based method like the mutual subspace method (MSM). In these methods, an image set is compactly represented by a subspace in high dimensional vector space, and the similarity between two image sets is calculated by using the canonical angles between two corresponding class subspaces. The key idea of MPWMSM is twofold. The first is to conduct multiple different whitening transformations of class subspaces in parallel as a natural extension of the whitened mutual subspace method (WMSM). The second is to discard a part of a sum space of class subspaces in forming the whitening transformation to increase the classification ability and the robustness against noise. We demonstrate the effectiveness of our method on tasks of 3D object classification using multi-view images and hand-gesture recognition and further verify the validity of the combination with CNN features through the Youtube Face dataset (YTF) recognition experiment.

Paper Citation

in Harvard Style

Yamaguchi O. and Fukui K. (2022). Image-set based Classification using Multiple Pseudo-whitened Mutual Subspace Method. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 296-306. DOI: 10.5220/0010836500003122

in Bibtex Style

@conference{icpram22,
author={Osamu Yamaguchi and Kazuhiro Fukui},
title={Image-set based Classification using Multiple Pseudo-whitened Mutual Subspace Method},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={296-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010836500003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Image-set based Classification using Multiple Pseudo-whitened Mutual Subspace Method
SN - 978-989-758-549-4
AU - Yamaguchi O.
AU - Fukui K.
PY - 2022
SP - 296
EP - 306
DO - 10.5220/0010836500003122