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Conference papers

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

Adeline Bailly 1, 2 Simon Malinowski 3 Romain Tavenard 1, 2 Thomas Guyet 4 Laetitia Chapel 1
1 OBELIX - Environment observation with complex imagery
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, UBS - Université de Bretagne Sud
2 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
4 DREAM - Diagnosing, Recommending Actions and Modelling
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques.
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Submitted on : Tuesday, August 18, 2015 - 12:03:10 PM
Last modification on : Wednesday, April 27, 2022 - 4:07:13 AM
Long-term archiving on: : Thursday, November 19, 2015 - 10:54:18 AM

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Adeline Bailly, Simon Malinowski, Romain Tavenard, Thomas Guyet, Laetitia Chapel. Bag-of-Temporal-SIFT-Words for Time Series Classification. ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2015, Porto, Portugal. ⟨halshs-01184900⟩

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