In this paper, we develop a video retrieval method based on Query-By-Example (QBE) approach where a query is represented by providing example shots. Relevant shots to the query are then retrieved by constructing a retrieval model from example shots. However, one drawback of QBE is that a user can only provide a small number of example shots, while each shot is generally represented by a high-dimensional feature. In such a case, a retrieval model tends to be overfit to feature dimensions which are specific to example shots, but are ineffective for retrieving relevant shots. As a result, many clearly irrelevant shots are retrieved. To overcome this, we construct a video ontology as a knowledge base for QBE-based video retrieval. Specifically, our video ontology is used to select concepts related to a query. Then, irrelevant shots are filtered by referring to recognition results of objects corresponding to selected concepts. Lastly, QBE-based video retrieval is performed on the remaining shots to obtain a final retrieval result. The effectiveness of our video ontology is tested on TRECVID 2009 video data. © 2011 Springer-Verlag.
CITATION STYLE
Shirahama, K., & Uehara, K. (2011). Effectiveness of video ontology in query by example approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6890 LNCS, pp. 49–58). https://doi.org/10.1007/978-3-642-23620-4_9
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