IEICE TRANSACTIONS on Information
Local Subspace Classifier with Transform-Invariance for Image Classification
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.6 pp.1756-1763
- Publication Date
- 2008/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.6.1756
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
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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},}
Copy
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 -
English
