Ontology-enriched semantic space for video search
Year: 2007
Type: conference-paper
Abstract: Multimedia-based ontology construction and reasoning have recently been recognized as two important issues in video search, particularly for bridging semantic gap. The lack of coincidence between low-level features and user expectation makes concept-based ontology reasoning an attractive mid-level framework for interpreting high-level semantics. In this paper, we propose a novel model, namely ontology-enriched semantic space (OSS), to provide a computable platform for modeling and reasoning concepts in a linear space. OSS enlightens the possibility of answering conceptual questions such as a high coverage of semantic space with minimal set of concepts, and the set of concepts to be developed for video search. More importantly, the query-to-concept mapping can be more reasonably conducted by guaranteeing the uniform and consistent comparison of concept scores for video search. We explore OSS for several tasks including concept-based video search, word sense disambiguation and multi-modality fusion. Our empirical findings show that OSS is a feasible solution to timely issues such as the measurement of concept combination and query-concept dependent fusion.
Authors: Xiao-Yong Wei, Chong-Wah Ngo
Institution: City University of Hong Kong
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2058041586