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Dynamic Two-Stage Image Retrieval from Large Multimodal Databases

dc.contributor.authorArampatzis, Avi
dc.contributor.authorZagoris, Konstantinos
dc.contributor.authorChatzichristofis, Savvas A.
dc.description.abstractContent-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. We experiment with a method for image retrieval from multimodal databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary modalities. We perform retrieval in a two-stage fashion: first rank by a secondary modality, and then perform CBIR only on the top-K items. Thus, effectiveness is improved by performing CBIR on a ‘better’ subset. Using a relatively ‘cheap’ first stage, efficiency is also improved via the fewer CBIR operations performed. Our main novelty is that K is dynamic, i.e. estimated per query to optimize a predefined effectiveness measure. We show that such dynamic two-stage setups can be significantly more effective and robust than similar setups with static thresholds previously proposeden_UK
dc.relation.ispartofseries33rd European Conference on IR Research, ECIR 2011;Advances in Information Retrieval, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings
dc.relation.ispartofseriesPart of the Lecture Notes in Computer Science book series (LNCS, volume 6611) pp. 326-337;
dc.rights© Springer-Verlag Berlin Heidelberg 2011en_UK
dc.subjectResearch Subject Categories::TECHNOLOGYen_UK
dc.subjectImage Retrievalen_UK
dc.subjecttop-K itemsen_UK
dc.titleDynamic Two-Stage Image Retrieval from Large Multimodal Databasesen_UK
dc.typeWorking Paperen_UK

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© Springer-Verlag Berlin Heidelberg 2011
Except where otherwise noted, this item's license is described as © Springer-Verlag Berlin Heidelberg 2011