Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2490257.2490271 | BCI |
Keywords | Field | DocType |
various folk dance,art method,folk dance culture,different dance,greek folk dance,words approach,dance video,specific dance,different style,general activity recognition area,folk dance,stip feature,folk dance recognition,multi agent system,simulation,social organization,reinforcement learning | Bag-of-words model,Data mining,Dance,General activity,Folk dance,Computer science,Feature extraction,Natural language processing,Artificial intelligence,Svm classifier,Cluster analysis | Conference |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Kapsouras | 1 | 46 | 4.03 |
| Stylianos Karanikolos | 2 | 7 | 0.79 |
| Nikolaos Nikolaidis | 3 | 108 | 10.31 |
| Anastasios Tefas | 4 | 2055 | 177.05 |