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An approach using many intermediate semantic concepts is proposed with the potential to bridge the semantic gap between what a color, shape, and texture-based ldquolow-levelrdquo image analysis can extract from video and what users really want to find, most likely using text descriptions of their information needs. Semantic concepts such as cars, planes, roads, people, animals, and different types of scenes (outdoor, night time, etc.) can be automatically detected in the video with reasonable accuracy. This leads us to ask how can they be used automatically and how does a user (or a retrieval system) translate the user's information need into a selection of related concepts that would help find the relevant video clips, from the large list of available concepts. We illustrate how semantic concept retrieval can be automatically exploited by mapping queries into query classes and through pseudo-relevance feedback. We also provide evidence how a semantic concept can be utilized by users in interactive retrieval, through interfaces that provide affordances of explicit concept selection and search, concept filtering, and relevance feedback. How many concepts we actually need and how accurately they need to be detected and linked through various relationships is specified in the ontology structure.
Date of Publication: April 2008