TY - CPAPER
KW - Artificial intelligence
KW - Back-ground knowledge
KW - Intangible cultural heritages
KW - Malaysians
KW - ontologies
KW - Ontology
KW - Query answering
KW - Querying
KW - Semantics
KW - Video Annotation
KW - Video annotations
KW - Video datasets
KW - Video training
AU - Sihem Belabbes
AU - Chi Tan
AU - Tri-Thuc Vo
AU - Yacine Izza
AU - Karim Tabia
AU - Sylvain Lagrue
AU - Salem Benferhat
AU - IEEE
AB - 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.
C2 - Proc. Int. Conf. Tools Artif. Intell. ICTAI
DO - 10.1109/ICTAI.2019.00239
LA - English
N1 - Journal Abbreviation: Proc. Int. Conf. Tools Artif. Intell. ICTAI
N2 - 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.
PB - IEEE Computer Society
PY - 2019
SN - 10823409 (ISSN); 9781728137988 (ISBN)
SP - 1638
EP - 1642
TI - Query Answering from Traditional Dance Videos: Case Study of Zapin Dances
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081078720&doi=10.1109%2fICTAI.2019.00239&partnerID=40&md5=b70bff9d438c3dae6cae56cd746fab0c
VL - 2019-November
ER -