Tackling temporal pattern recognition by vector space embedding

Brian Iwana, Seiichi Uchida, Kaspar Riesen, Volkmar Frinken

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

This paper introduces a novel method of reducing the number of prototype patterns necessary for accurate recognition of temporal patterns. The nearest neighbor (NN) method is an effective tool in pattern recognition, but the downside is it can be computationally costly when using large quantities of data. To solve this problem, we propose a method of representing the temporal patterns by embedding dynamic time warping (DTW) distance based dissimilarities in vector space. Adaptive boosting (AdaBoost) is then applied for classifier training and feature selection to reduce the number of prototype patterns required for accurate recognition. With a data set of handwritten digits provided by the International Unipen Foundation (iUF), we successfully show that a large quantity of temporal data can be efficiently classified produce similar results to the established NN method while performing at a much smaller cost.

本文言語英語
ホスト出版物のタイトル13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
出版社IEEE Computer Society
ページ816-820
ページ数5
ISBN(電子版)9781479918058
DOI
出版ステータス出版済み - 11 20 2015
イベント13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, フランス
継続期間: 8 23 20158 26 2015

出版物シリーズ

名前Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
2015-November
ISSN(印刷版)1520-5363

その他

その他13th International Conference on Document Analysis and Recognition, ICDAR 2015
Countryフランス
CityNancy
Period8/23/158/26/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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