not logged in. (login)

Document Information

Filtering Methods for Similarity-Based Multimedia Retrieval (2005) Vassilis Athitsos, Jonathan Alon, Stan Sclaroff and George Kollios	Download ps/pdf
Abstract: A common problem in multimedia databases is retrieving the most similar matches to a query object. Finding those matches can be too slow to be practical, especially in domains where comparing multimedia objects involves computationally expensive similarity (or distance) measures. Filter-and-refine retrieval is a framework for addressing this problem: the filter step quickly filters out most database objects, and the refine step identifies the best matches among the remaining candidates. This paper describes two filtering methods, that work by constructing efficient approximations of computationally expensive similarity measures. The first method can be applied to arbitrary domains, and the second method explicitly targets domains where measuring similarity includes an alignment process. The benefits of these two filtering methods are illustrated in experiments with databases from different domains, i.e., hand images, gesture videos, and online digit recognition for hand-held devices.
Published in: Proceedings of the Seventh International Workshop of the EU Network of Excellence DELOS on Audio-Visual Content and Information Visualization in Digital Libraries (AVIVDiLib), pp (10 pages), 2005.