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Video Ontology
Abstract
There has been a great deal of interest in the development of ontology to facilitate knowledge sharing and database integration. In general, ontology is a set of terms or vocabularies of interest in a particular information domain, and shows the relationships among them (Doerr, Hunter, & Lagoze, 2003). It includes machine-interpretable definitions of basic concepts in the domain. Ontology is very popular in the fields of natural language processing (NLP) and Web user interface (Web ontology). To take this advantage into multimedia content analysis, several studies have proposed ontology-based schemes (Hollink & Worring, 2005; Spyropoulos, Paliouras, Karkaletsis, Kosmopoulos, Pratikakis, Perantonis, & Gatos, 2005). Modular structure of the ontology methodology is used in a generic analysis scheme to semantically interpret and annotate multimedia content. This methodology consists of domain ontology, core ontology, and multimedia ontology. Domain ontology captures concepts in a particular type of domain, while core ontology is the key building blocks necessary to enable the scalable assimilation of information from diverse sources. Multimedia ontology is used to model multimedia data, such as audio, image, and video. In the multimedia data analysis the meaningful patterns and hidden knowledge are discovered from the database. There are existing tools for managing and searching the discovered patterns and knowledge. However, almost all of the approaches use low-level feature values instead of high-level perceptions, which make a huge gap between machine interpretation and human understanding. For example, if we have to retrieve anomaly from video surveillance systems, low-level feature values cannot represent such semantic meanings. In order to address the problem, the main focus of research has been on the construction and utilization of ontology for specific data domain in various applications. In this chapter, we first survey the state-of-the-art in multimedia ontology, specifically video ontology, and then investigate the methods of automatic generation of video ontology.
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