Folk dance recognition using a bag of words approach and ISA/STIP features

Authors:
Ioannis Kapsouras;Stylianos Karanikolos;Nikolaos Nikolaidis;Anastasios Tefas
Affiliations:
Aristotle University of Thessaloniki, Thessaloniki, Greece;Aristotle University of Thessaloniki, Thessaloniki, Greece;Aristotle University of Thessaloniki, Thessaloniki, Greece;Aristotle University of Thessaloniki, Thessaloniki, Greece
Venue:
Proceedings of the 6th Balkan Conference in Informatics
Year:
2013

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Abstract

Recognition of folk dances i.e. classification of dance videos according to the specific dance depicted can be considered a challenging sub task within the general activity recognition area because of the large number of different dances, the similarities among them and the different styles a dance can be performed. A method able to identify various folk dances is very important for analyzing and annotating multimedia databases of such dances thus helping the preservation of folk dance culture. In this paper, we deal with recognition of Greek folk dances. Clustering is applied on input features to extract a codedbook and a bag of words approach is applied. An SVM classifier is used for the classification. Two state of the art methods for feature extraction are used and compared. The method is applied on two folk dances from the Western Macedonia region.