Distinct nearest neighbors queries for similarity search in very large multimedia databases

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This publication doesn't include Faculty of Economics and Administration. It includes Faculty of Informatics. Official publication website can be found on muni.cz.
Authors

SKOPAL Tomáš DOHNAL Vlastislav BATKO Michal ZEZULA Pavel

Year of publication 2009
Type Article in Proceedings
Conference 11th ACM International Workshop on Web Information and Data Management (WIDM 2009)
MU Faculty or unit

Faculty of Informatics

Citation
Web http://portal.acm.org/citation.cfm?id=1651592
Field Informatics
Keywords similarity search;kNN query;content-based retrieval
Description As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates.
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