BRIEF-Based Mid-Level Representations for Time Series Classification
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.
KeywordsTime series Mid-level representations BRIEF-based descriptors
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.
- 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
- 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
- 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.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
- 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