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

  • Adeline BaillyEmail author
  • Simon MalinowskiEmail author
  • Romain Tavenard
  • Laetitia Chapel
  • Thomas Guyet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9785)


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.


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



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.


  1. 1.
    Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2918 (2012)Google Scholar
  2. 2.
    Bagnall, A., Bostrom, A., Large, J., Lines, J.: The great time series classification bake off: an experimental evaluation of recently proposed algorithms. Extended Version. CoRR, abs/1602.01711 (2016)Google Scholar
  3. 3.
    Bagnall, A., Lines, J., Hills, J., Bostrom, A.: Time-series classification with COTE: the collective of transformation-based ensembles. IEEE Trans. Knowl. Data Eng. 27(9), 2522–2535 (2015)CrossRefGoogle Scholar
  4. 4.
    Bailly, A., Malinowski, S., Tavenard, R., Guyet, T., Chapel, L.: Bag-of-Temporal-SIFT-Words for time series classification. In: Proceedings of the ECML-PKDD Workshop on Advanced Analytics and Learning on Temporal Data (2015)Google Scholar
  5. 5.
    Baydogan, M.G., Runger, G.: Learning a symbolic representation for multivariate time series classification. Data Min. Knowl. Discov. 29(2), 400–422 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Baydogan, M.G., Runger, G., Tuv, E.: A Bag-of-Features framework to classify time series. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2796–2802 (2013)CrossRefGoogle Scholar
  7. 7.
    Bosch, A., Zisserman, A., Muoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Anal. Mach. Intell. 30(4), 712–727 (2008)CrossRefGoogle Scholar
  8. 8.
    Candan, K.S., Rossini, R., Sapino, M.L.: sDTW: computing DTW distances using locally relevant constraints based on salient feature alignments. In: Proceedings of the International Conference on Very Large DataBases, vol. 5, pp. 1519–1530 (2012)Google Scholar
  9. 9.
    Cuturi, M.: Fast global alignment kernels. In: Proceedings of the International Conference on Machine Learning, pp. 929–936 (2011)Google Scholar
  10. 10.
    Douzal-Chouakria, A., Amblard, C.: Classification trees for time series. Elsevier Pattern Recogn. 45(3), 1076–1091 (2012)CrossRefGoogle Scholar
  11. 11.
    Dusseux, P., Corpetti, T., Hubert-Moy, L.: Temporal kernels for the identification of grassland management using time series of high spatial resolution satellite images. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 3258–3260 (2013)Google Scholar
  12. 12.
    Jégou, H., Chum, O.: Negative evidences and co-occurrences in image retrieval: the benefit of PCA and whitening. In: Proceedings of the European Conference on Computer Vision, pp. 774–787 (2012)Google Scholar
  13. 13.
    Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3304–3311 (2010)Google Scholar
  14. 14.
    Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: Proceedings of the International Conference on Computer Vision, pp. 604–610 (2005)Google Scholar
  15. 15.
    Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering Homepage (2011).
  16. 16.
    Le Cun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 255–258. MIT Press, Cambrdige (1995)Google Scholar
  17. 17.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)Google Scholar
  18. 18.
    Lin, J., Khade, R., Li, Y.: Rotation-invariant similarity in time series using Bag-of-Patterns representation. Int. J. Inf. Syst. 39, 287–315 (2012)CrossRefGoogle Scholar
  19. 19.
    Lines, J., Bagnall, A.: Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Dis. 29(3), 565–592 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, pp. 1150–1157 (1999)Google Scholar
  21. 21.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  22. 22.
    Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  23. 23.
    Perronnin, F., Liu, Y., Sanchez, J., Poirier, H.: Large-scale image retrieval with compressed Fisher vectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3384–3391 (2010)Google Scholar
  24. 24.
    Ratanamahatana, C.A., Keogh, E.: Everything you know about dynamic time warping is wrong. In: Proceedings of the Workshop on Mining Temporal and Sequential Data, pp. 22–25 (2004)Google Scholar
  25. 25.
    Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Dis. 29(6), 1505–1530 (2014)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Senin, P., Malinchik, S.: SAX-VSM: interpretable time series classification using SAX and vector space model. In: Proceedings of the IEEE International Conference on Data Mining, pp. 1175–1180 (2013)Google Scholar
  27. 27.
    Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of the International Conference on Computer Vision, pp. 1470–1477 (2003)Google Scholar
  28. 28.
    Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: Proceedings of the British Machine Vision Conference, pp. 124.1–124.11 (2009)Google Scholar
  29. 29.
    Wang, J., Liu, P., She, M.F.H., Nahavandi, S., Kouzani, A.: Bag-of-Words representation for biomedical time series classification. Biomed. Sig. Process. Control 8(6), 634–644 (2013)CrossRefGoogle Scholar
  30. 30.
    Xie, J., Beigi, M.: A scale-invariant local descriptor for event recognition in 1D sensor signals. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1226–1229 (2009)Google Scholar
  31. 31.
    Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956 (2009)Google Scholar

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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