BUDÍKOVÁ, Petra, Michal BATKO and Pavel ZEZULA. Improving the Image Retrieval System by Ranking. In 3rd International Conference on Similarity Search and Applications (SISAP 2010). New York: ACM Press, 2010, p. 123-124. ISBN 978-1-4503-0420-7.
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Basic information
Original name Improving the Image Retrieval System by Ranking
Name in Czech Systém pro vyhledávání v obrázcích s vylepšeným uspořádáním výsledků
Authors BUDÍKOVÁ, Petra (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
Edition New York, 3rd International Conference on Similarity Search and Applications (SISAP 2010), p. 123-124, 2 pp. 2010.
Publisher ACM Press
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Turkey
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14330/10:00044842
Organization unit Faculty of Informatics
ISBN 978-1-4503-0420-7
Keywords in English image retrieval system; ranking; relevance-feedback
Tags DISA
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 10/3/2016 11:27.
Abstract
This demonstration paper presents an image retrieval system that allows to bridge the semantic gap by combining visual and text ranking on a large collection of 8 million images. We present several ranking methods including user relevance feedback. The demonstration is available as online web application.
Links
GA201/09/0683, research and development projectName: Vyhledávání v rozsáhlých multimediálních databázích
Investor: Czech Science Foundation, Similarity Searching in Very Large Multimedia Databases
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
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