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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content
verfasst von : Giannis Chantas, Alexandros Kitsikidis, Spiros Nikolopoulos, Kosmas Dimitropoulos, Stella Douka, Ioannis Kompatsiaris, Nikos Grammalidis
Erschienen in: Computer Vision - ECCV 2014 Workshops
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In this paper we introduce Multi-Entity Bayesian Networks (MEBNs) as the means to combine first-order logic with probabilistic inference and facilitate the semantic analysis of Intangible Cultural Heritage (ICH) content. First, we mention the need to capture and maintain ICH manifestations for the safeguarding of cultural treasures. Second, we present the MEBN models and stress their key features that can be used as a powerful tool for the aforementioned cause. Third, we present the methodology followed to build a MEBN model for the analysis of a traditional dance. Finally, we compare the efficiency of our MEBN model with that of a simple Bayesian network and demonstrate its superiority in cases that demand for situation-specific treatment.