The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une famille de classificateurs de sous-espace linéaires appelés classificateurs de sous-espace locaux (LSC) surpasse les k-règle du voisin le plus proche (kNN) et les classificateurs de sous-espaces conventionnels dans la classification des chiffres manuscrits. Cependant, LSC souffre d'une très grande sensibilité aux transformations d'images car il utilise la projection et les distances euclidiennes pour la classification. Dans cet article, je présente une combinaison d'un classificateur de sous-espace local (LSC) et d'une distance tangente (TD) pour améliorer la précision de la reconnaissance des chiffres manuscrits. Dans cette règle de classification, nous pouvons facilement traiter l'invariance par transformation car nous sommes capables d'utiliser des vecteurs tangents pour l'approximation des transformations. Cependant, nous ne pouvons pas utiliser de vecteurs tangents dans d’autres types d’images telles que les images couleur. Par conséquent, le LSC du noyau (KLSC) est proposé pour incorporer l'invariance de transformation dans le LSC via le mappage du noyau. Les performances des méthodes proposées sont vérifiées avec des expériences sur la classification des chiffres manuscrits et des images couleur.
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Seiji HOTTA, "Local Subspace Classifier with Transform-Invariance for Image Classification" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 6, pp. 1756-1763, June 2008, doi: 10.1093/ietisy/e91-d.6.1756.
Abstract: A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.6.1756/_p
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@ARTICLE{e91-d_6_1756,
author={Seiji HOTTA, },
journal={IEICE TRANSACTIONS on Information},
title={Local Subspace Classifier with Transform-Invariance for Image Classification},
year={2008},
volume={E91-D},
number={6},
pages={1756-1763},
abstract={A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.},
keywords={},
doi={10.1093/ietisy/e91-d.6.1756},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Local Subspace Classifier with Transform-Invariance for Image Classification
T2 - IEICE TRANSACTIONS on Information
SP - 1756
EP - 1763
AU - Seiji HOTTA
PY - 2008
DO - 10.1093/ietisy/e91-d.6.1756
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2008
AB - A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.
ER -