Example-based event retrieval in video archive using rough set theory and video ontology
Kimiaki Shirahama, Kuniaki Uehara · 2010
In this paper, we develop a method for retrieving events of interest in a video archive. To this end, we address the following two issues. First, due to camera techniques, locations and so on, shots of an event contain significantly different features. So, they cannot be retrieved by a single retrieval model. Thus, we use "rough set theory" to extract multiple classification rules, each of which correctly identifies a subset of shots of the event. Second, although concepts like Person, Car and Cityspace are useful for event retrieval, we need to distinguish between relevant concepts to an event and irrelevant ones. Otherwise, the retrieval performance degrades. So, in order to select concepts relevant to the event, we organize concepts into "video ontology" which is a formal and explicit specification of concepts, concept properties and relations among concepts. Experimental results show both the effectiveness of rough set theory and the one of video ontology.