BATKO, Michal, Petra KOHOUTKOVÁ and Pavel ZEZULA. Combining Metric Features in Large Collections. In 1st International Workshop on Similarity Search and Applications (SISAP 2008). Los Alamitos CA, Washington, Tokyo: IEEE Computer Society, 2008, p. 79-86. ISBN 978-0-7695-3101-4.
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Basic information
Original name Combining Metric Features in Large Collections
Name in Czech Kombinování metrických charakteristik ve velkých kolekcích dat
Authors BATKO, Michal (203 Czech Republic, guarantor), Petra KOHOUTKOVÁ (203 Czech Republic) and Pavel ZEZULA (203 Czech Republic).
Edition Los Alamitos CA, Washington, Tokyo, 1st International Workshop on Similarity Search and Applications (SISAP 2008), p. 79-86, 8 pp. 2008.
Publisher IEEE Computer Society
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Mexico
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14330/08:00024185
Organization unit Faculty of Informatics
ISBN 978-0-7695-3101-4
UT WoS 000255509900009
Keywords in English similarity search; complex query; p2p network; approximation
Tags APPROXIMATION, complex query, DISA, p2p network, similarity search
Tags International impact, Reviewed
Changed by Changed by: RNDr. Michal Batko, Ph.D., učo 2907. Changed: 19/6/2009 16:21.
Abstract
Current information systems are required to process complex digital objects, which are typically characterized by multiple descriptors. Since the values of many descriptors belong to non-sortable domains, they are effectively comparable only by a sort ofsimilarity. Moreover, the scalability is very important in the current digital-explosion age. Therefore, we propose a distributed extension of the well-known threshold algorithm for peer-to-peer paradigm. The technique allows to answer similarity queries that combine multiple similarity measures and due to its peer-to-peer nature it is highly scalable. We also explore possibilities of approximate evaluation strategies, where some relevant results can be lost in favor of increasing the efficiency by order of magnitude. To reveal the strengths and weaknesses of our approach we have experimented with a 1.6 million image database from Flicker comparing the content of the images by five similarity measures from the MPEG-7 standard. To the best of our knowledge, the experience with such a huge real-life dataset is quite unique.
Abstract (in Czech)
Článek popisuje rozšíření existujícího "prahovacího" algoritmu pro prostředí peer-to-peer sítí. Technika umožňuje řešit podobnostní dotazy kombinující několik podobnostních měřítek a díky využití peer-to-peer technologie je vysoce škálovatelná. Dále jsou v článku rozebírany přínosy aproximativní strategie. Výsledky jsou ověřeny na databázi s 1,6 miliony obrázků ze systému Flickr.
Links
GP201/08/P507, research and development projectName: Komplexní podobnostní dotazy nad rozsáhlými objemy dat
Investor: Czech Science Foundation, Complex similarity searching in very large data collections
1ET100300419, research and development projectName: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu
Investor: Academy of Sciences of the Czech Republic, Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)
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