Introducing local distance-based features to temporal convolutional neural networks

Brian Kenji Iwana, Minoru Mori, Akisato Kimura, Seiichi Uchida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose the use of local distance-based features determined by Dynamic Time Warping (DTW) for temporal Convolutional Neural Networks (CNN). Traditionally, DTW is used as a robust distance metric for time series patterns. However, this traditional use of DTW only utilizes the scalar distance metric and discards the local distances between the dynamically matched sequence elements. This paper proposes recovering these local distances, or DTW features, and utilizing them for the input of a CNN. We demonstrate that these features can provide additional information for the classification of isolated handwritten digits and characters. Furthermore, we demonstrate that the DTW features can be combined with the spatial coordinate features in multi-modal fusion networks to achieve state-of-the-art accuracy on the Unipen online handwritten character datasets.

Original languageEnglish
Title of host publicationProceedings - 2018 16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages92-97
Number of pages6
ISBN (Electronic)9781538658758
DOIs
Publication statusPublished - Dec 5 2018
Event16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018 - Niagara Falls, United States
Duration: Aug 5 2018Aug 8 2018

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume2018-August
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Other

Other16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
CountryUnited States
CityNiagara Falls
Period8/5/188/8/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Iwana, B. K., Mori, M., Kimura, A., & Uchida, S. (2018). Introducing local distance-based features to temporal convolutional neural networks. In Proceedings - 2018 16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018 (pp. 92-97). [8563232] (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR-2018.2018.00025