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)
Adeline Bailly
- Fonction : Auteur
- PersonId : 8984
- IdHAL : adeline-bailly
- IdRef : 227850564
Simon Malinowski
- Fonction : Auteur
- PersonId : 14415
- IdHAL : simon-malinowski
- IdRef : 12983436X
Romain Tavenard
- Fonction : Auteur
- PersonId : 5645
- IdHAL : rtavenar
- ORCID : 0000-0002-1439-8465
- IdRef : 154729507
Thomas Guyet
- Fonction : Auteur
- PersonId : 4817
- IdHAL : thomas-guyet
- ORCID : 0000-0002-4909-5843
- IdRef : 171949307
Laetitia Chapel
- Fonction : Auteur
- PersonId : 740638
- IdHAL : laetitia-chapel
- IdRef : 120235943
Résumé
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.
Domaines
Méthodes et statistiquesFormat du dépôt | Fichier |
---|---|
Type de dépôt | Communication dans un congrès |
Titre |
en
Bag-of-Temporal-SIFT-Words for Time Series Classification
|
Résumé |
en
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.
|
Auteur(s) |
Adeline Bailly
1, 2
, Simon Malinowski
3
, Romain Tavenard
1, 2
, Thomas Guyet
4
, Laetitia Chapel
1
1
OBELIX -
Environment observation with complex imagery
( 255395 )
- Campus de Tohannic, 56017 Vannes Cedex
- France
2
LETG - Rennes -
Littoral, Environnement, Télédétection, Géomatique
( 3177 )
- Maison de la Recherche Place du Recteur Henri Le Moal 35043 RENNES CEDEX
- France
3
LinkMedia -
Creating and exploiting explicit links between multimedia fragments
( 407335 )
- France
4
DREAM -
Diagnosing, Recommending Actions and Modelling
( 2516 )
- Campus de Beaulieu 35042 Rennes cedex
- France
|
Langue du document |
Anglais
|
Date de production/écriture |
2015-07-29
|
Vulgarisation |
Non
|
Actes |
Oui
|
Comité de lecture |
Oui
|
Invité |
Non
|
Audience |
Internationale
|
Titre du congrès |
ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data
|
Date début congrès |
2015-09-11
|
Ville |
Porto
|
Pays |
Portugal
|
Domaine(s) |
|
Financement |
|
Projet(s) ANR |
|
Mots-clés |
en
SIFT, BoTSW, time series classification, Bag-of-Words
|
Origine :
Fichiers produits par l'(les) auteur(s)
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