Exploring Multimedia Databases via Optimization-Based Relevance Feedback and the Earth Mover's Distance

Determining similar objects is a fundamental operation both in data mining tasks such as clustering and in query-driven object retrieval. By definition of similarity search, query objects can only be imprecise descriptions of what users are looking for in a database, and even high-quality similarity measures can only be approximations of the users’ notion of similarity. To overcome these shortcomings, iterative query refinement systems have been proposed. They utilize user feedback regarding the relevance of intermediate results to adapt the query object and/or the similarity measure.

We propose an optimization-based relevance feedback approach for adaptable distance measures – focusing on the Earth Mover’s Distance. Our technique enables quicker iterative database exploration as shown by our experiments.


© ACM, 2009. 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 ACM CIKM 2009. http://doi.acm.org/10.1145/1645953.1646187

Authors: Wichterich M., Beecks C., Sundermeyer M., Seidl T.
Published in: Proc. 18th ACM Conference on Information and Knowledge Management (CIKM 2009), Hong Kong, China.
Publisher: ACM - New York, NY, USA
Sprache: EN
Jahr: 2009
Seiten: 1621-1624
ISBN: 978-1-60558-512-3
Konferenz: CIKM
DOI: 10.1145/1645953.1646187
Typ: Tagungsbeiträge
Forschungsgebiet: Exploration of Multimedia Databases