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Evaluation of Bag-of-Word Performance for Time Series Classification Using Discriminative SIFT-Based Mid-Level Representations

  • Raquel Almeida
  • Hugo Herlanin
  • Zenilton Kleber G. do PatrocinioJr.Email author
  • Simon Malinowski
  • Silvio Jamil Ferzoli Guimarães
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Time series classification has been widely explored over the last years. Amongst the best approaches for that task, many are based on the Bag-of-Words framework, in which time series are transformed into a histogram of word occurrences. These words represent quantized features that are extracted beforehand. In this paper, we aim to evaluate the use of accurate mid-level representations in order to enhance the Bag-of-Words representation. More precisely, this kind of representation enables to reduce the loss induced by feature quantization. Experiments show that these representations are likely to improve time series classification accuracy compared to Bag-of-Words and some of them are very competitive to the state-of-the-art.

Keywords

Time series Mid-level representations SIFT-based descriptors 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Raquel Almeida
    • 1
  • Hugo Herlanin
    • 1
  • Zenilton Kleber G. do PatrocinioJr.
    • 1
    Email author
  • Simon Malinowski
    • 2
  • Silvio Jamil Ferzoli Guimarães
    • 1
  1. 1.Computer Science DepartmentPontifical Catholic University of Minas GeraisBelo HorizonteBrazil
  2. 2.Université de Rennes 1, IRISARennesFrance

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