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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 UMR 6554
2 OBELIX - Environment observation with complex imagery
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, UBS - Université de Bretagne Sud
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
4 LACODAM - Large Scale Collaborative Data Mining
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE, Inria Rennes – Bretagne Atlantique
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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Submitted on : Wednesday, May 25, 2016 - 2:17:46 PM
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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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