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@inproceedings{867524,
author = {Skopal, Tomáš and Dohnal, Vlastislav and Batko, Michal and Zezula, Pavel},
address = {New York, USA},
booktitle = {11th ACM International Workshop on Web Information and Data Management (WIDM 2009)},
keywords = {similarity search;kNN query;content-based retrieval},
language = {eng},
location = {New York, USA},
isbn = {978-1-60558-808-7},
pages = {11-14},
publisher = {ACM},
title = {Distinct nearest neighbors queries for similarity search in very large multimedia databases},
url = {http://portal.acm.org/citation.cfm?id=1651592},
year = {2009}
}
TY - JOUR
ID - 867524
AU - Skopal, Tomáš - Dohnal, Vlastislav - Batko, Michal - Zezula, Pavel
PY - 2009
TI - Distinct nearest neighbors queries for similarity search in very large multimedia databases
PB - ACM
CY - New York, USA
SN - 9781605588087
KW - similarity search;kNN query;content-based retrieval
UR - http://portal.acm.org/citation.cfm?id=1651592
N2 - 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.
ER -
SKOPAL, Tomáš, Vlastislav DOHNAL, Michal BATKO and Pavel ZEZULA. Distinct nearest neighbors queries for similarity search in very large multimedia databases. In \textit{11th ACM International Workshop on Web Information and Data Management (WIDM 2009)}. New York, USA: ACM. p.~11-14. ISBN~978-1-60558-808-7. 2009.
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