Advertisement

BRIEF-Based Mid-Level Representations for Time Series Classification

  • Renato Souza
  • Raquel Almeida
  • Roberto Miranda
  • Zenilton Kleber G. do PatrocinioJr.
  • Simon Malinowski
  • Silvio Jamil F. GuimarãesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

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 representation called BossaNova in order to enhance the Bag-of-Words representation and to propose a new binary time series descriptor, called BRIEF-based descriptor. More precisely, this kind of representation enables to reduce the loss induced by feature quantization. Experiments show that this representation in conjunction to BRIEF-based descriptor is statistically equivalent to traditional Bag-of-Words, in terms time series classification accuracy, being about 4 times faster. Furthermore, it is very competitive when compared to the state-of-the-art.

Keywords

Time series Mid-level representations BRIEF-based descriptors 

Notes

Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. Moreover, the authors are grateful to PUC Minas, FAPEMIG and the TRANSFORM project funded by CAPES/STIC-AMSUD (18-STIC-09) for the partial financial support to this work.

References

  1. 1.
    Almeida, R., Herlanin, H., do Patrocinio, Z.K.G., Malinowski, S., Guimarães, S.J.F.: Evaluation of bag-of-word performance for time series classification using discriminative sift-based mid-level representations. In: Vera-Rodriguez, R., Fierrez, J., Morales, A. (eds.) CIARP 2018. LNCS, vol. 11401, pp. 109–116. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-13469-3_13CrossRefGoogle Scholar
  2. 2.
    Avila, S., Thome, N., Cord, M., Valle, E., AraúJo, A.D.A.: Pooling in image representation: the visual codeword point of view. Comput. Vis. Image Underst. 117(5), 453–465 (2013)CrossRefGoogle Scholar
  3. 3.
    Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining Knowl. Discov. 31(3), 606–660 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Bailly, A., Malinowski, S., Tavenard, R., Chapel, L., Guyet, T.: Dense bag-of-temporal-SIFT-words for time series classification. In: Douzal-Chouakria, A., Vilar, J.A., Marteau, P.-F. (eds.) AALTD 2015. LNCS (LNAI), vol. 9785, pp. 17–30. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-44412-3_2CrossRefGoogle Scholar
  6. 6.
    Baydogan, M.G., Runger, G., Tuv, E.: A bag-of-features framework to classify time series. IEEE PAMI 35(11), 2796–2802 (2013)CrossRefGoogle Scholar
  7. 7.
    Boureau, Y.L., Bach, F., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: Proceedings of the CVPR 2010, pp. 2559–2566 (2010)Google Scholar
  8. 8.
    Caetano, C., Avila, S., Guimaraes, S., Araújo, A.D.A.: Pornography detection using Bossanova video descriptor. In: Proceedings of the EUSIPCO 2014, pp. 1681–1685. IEEE, Lisbon (2014)Google Scholar
  9. 9.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15561-1_56CrossRefGoogle Scholar
  10. 10.
    Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Mining Knowl. Discov. 28(4), 851–881 (2014)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jegou, H., Perronnin, F., Douze, M., Sánchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE PAMI 34(9), 1704–1716 (2012)CrossRefGoogle Scholar
  12. 12.
    Lin, J., Khade, R., Li, Y.: Rotation-invariant similarity in time series using bag-of-patterns representation. J. Intell. Inf. Syst. 39(2), 287–315 (2012)CrossRefGoogle Scholar
  13. 13.
    Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Mining Knowl. Discov. 29(6), 1505–1530 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

Personalised recommendations