Dense Bag-of-Temporal-SIFT-Words for Time Series Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9785)

Abstract

The SIFT framework has shown to be effective in the image classification context. In [4], we designed a Bag-of-Words approach based on an adaptation of this framework to time series classification. It relies on two steps: SIFT-based features are first extracted and quantized into words; histograms of occurrences of each word are then fed into a classifier. In this paper, we investigate techniques to improve the performance of Bag-of-Temporal-SIFT-Words: dense extraction of keypoints and different normalizations of Bag-of-Words histograms. Extensive experiments show that our method significantly outperforms nearly all tested standalone baseline classifiers on publicly available UCR datasets.

Keywords

Time series classification Bag-of-Words SIFT Dense features BoTSW D-BoTSW 

Notes

Acknowledgments

This work has been partly funded by ANR project ASTERIX (ANR-13-JS02-0005-01), Région Bretagne and CNES-TOSCA project VEGIDAR. Authors also thank anonymous reviewers for their fruitful comments as well as data donators.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Université de Rennes 2, IRISA, LETG-Rennes COSTELRennesFrance
  2. 2.Université de Rennes 1, IRISARennesFrance
  3. 3.Université de Bretagne-Sud, IRISAVannesFrance
  4. 4.Agrocampus Ouest, IRISARennesFrance

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