Query Answering from Traditional Dance Videos: Case Study of Zapin Dances

Sihem Belabbes (1) , Chi Wee Tan , Tri-Thuc Vo , Yacine Izza (1) , Karim Tabia (1) , Sylvain Lagrue (2) , Salem Benferhat (1)

1 CRIL - Centre de Recherche en Informatique de Lens
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

The aim of this paper is to highlight two important issues related to the annotation and querying of Intangible Cultural Heritage video datasets. First, we focus on ontology completion by annotating dance videos. In order to build video training sets and to enrich the proposed ontology, manual video annotation is performed based on background knowledge formalized in an ontology, representing a semantics of a traditional dance. The paper provides a case study on Malaysian Zapin dances. Second, we address the question of how can end-users efficiently query the datasets of annotated videos that are built.

Mots clés: Video Annotation; Ontologies; Querying

Domaines: Intelligence artificielle [cs.AI]; Informatique et théorie des jeux [cs.GT]; Logique en informatique [cs.LO]

Sihem Belabbes, Chi Wee Tan, Tri-Thuc Vo, Yacine Izza, Karim Tabia, et al.. Query Answering from Traditional Dance Videos: Case Study of Zapin Dances. 31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2019), Nov 2019, Portland, United States. pp.1638-1642, ⟨10.1109/ICTAI.2019.00239⟩. ⟨hal-02417666⟩