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

Adeline Bailly 1, 2 Simon Malinowski 3 Romain Tavenard 1, 2 Laetitia Chapel 2 Thomas Guyet 4
1 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
4 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
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.
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Chapitre d'ouvrage
Advanced Analysis and Learning on Temporal Data, Springer, 2016, 978-3319444116. <10.1007/978-3-319-44412-3_2>
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https://hal.archives-ouvertes.fr/hal-01252726
Contributeur : Romain Tavenard <>
Soumis le : mercredi 25 mai 2016 - 14:17:46
Dernière modification le : lundi 27 février 2017 - 11:23:37

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Adeline Bailly, Simon Malinowski, Romain Tavenard, Laetitia Chapel, Thomas Guyet. Dense Bag-of-Temporal-SIFT-Words for Time Series Classification. Advanced Analysis and Learning on Temporal Data, Springer, 2016, 978-3319444116. <10.1007/978-3-319-44412-3_2>. <hal-01252726v4>

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