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dc.contributor.authorChen, Y.-
dc.contributor.authorDick, A.-
dc.contributor.authorLi, X.-
dc.contributor.authorHill, R.-
dc.contributor.editorCremers, D.-
dc.contributor.editorReid, I.-
dc.contributor.editorSaito, H.-
dc.contributor.editorYang, M.H.-
dc.identifier.citationLecture Notes in Artificial Intelligence, 2015 / Cremers, D., Reid, I., Saito, H., Yang, M.H. (ed./s), vol.9003, pp.196-210-
dc.description.abstractWe propose a simple but effective re-ranking method for improving the results of object retrieval. Our method considers the contextual information embedded in a dataset. This is based on the observation that if there are multiple images containing the same object in a dataset, then these images can often be grouped into clusters. We make the following two contributions. Firstly, we gain this contextual information by a random dimension partition of the dataset. This enables online query model expansion if needed. Secondly, we use the collected contextual information to refine the initial retrieval results by taking into account the context in which each retrieved image occurs. Experimental results on several datasets demonstrate the effectiveness of our method in both accuracy and computation cost: our method refines retrieval results without relying on low-level feature matching or re-issuing the query.-
dc.description.statementofresponsibilityYanzhi Chen, B, Anthony Dick, Xi Li, and Rhys Hill-
dc.publisherSpringer International Publishing-
dc.relation.ispartofseriesLecture Notes in Computer Science, (LNCS, vol. 9003)-
dc.rights© Springer International Publishing Switzerland 2015-
dc.titleContext based re-ranking for object retrieval-
dc.typeConference paper-
dc.contributor.conference12th Asian Conference on Computer Vision (ACCV 2014) (1 Nov 2014 - 5 Nov 2014 : Singapore)-
dc.identifier.orcidDick, A. [0000-0001-9049-7345]-
Appears in Collections:Aurora harvest 8
Computer Science publications

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