Local Subspace Classifier with Transform-Invariance for Image Classification

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2008-06-01

著者

• Hotta Seiji

  Tokyo Univ. Agriculture And Technol. Koganei‐shi Jpn

• Hotta Seiji

  Institute Of Symbiotic Science And Technology Tokyo University Of Agriculture And Technology

関連論文

• Color Image Classification Using Block Matching and Learning
• Color Image Classification Using Block Image Replacement and Local Averaging Classifier(Internationa Session 7)
• Video Classification Using Linear Subspace Methods(International Session 1)
• Local Subspace Classifier with Transform-Invariance for Image Classification

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