Adaptive image retrieval using a graph model for semantic feature integration

Urban, J. and Jose, J.M. (2006) Adaptive image retrieval using a graph model for semantic feature integration. In: 8th ACM International Workshop on Multimedia Information Retrieval MIR '06, Santa Barbara, CA, USA, 26-27 October 2006, pp. 117-126. ISBN 1595934952 (doi: 10.1145/1178677.1178696)

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Publisher's URL: http://doi.acm.org/10.1145/1178677.1178696

Abstract

The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents).

Item Type:Conference Proceedings
Additional Information:© ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 8th ACM International Workshop on Multimedia Information Retrieval MIR '06
Keywords:semantic features, image retrieval, relevance feedback, random walks, fusion
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon
Authors: Urban, J., and Jose, J.M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science
Research Group:Information Retrieval
Publisher:ACM Press
ISBN:1595934952
Copyright Holders:Copyright © 2006 ACM Press
First Published:First published in 8th ACM International Workshop on Multimedia Information Retrieval MIR '06
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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