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Massive query expansion by exploiting graph knowledge bases for image retrieval

Author
Guisado, J.; Dominguez, D.; Larriba, J.
Type of activity
Presentation of work at congresses
Name of edition
4th ACM International Conference on Multimedia Retrieval
Date of publication
2014
Presentation's date
2014-04-01
Book of congress proceedings
Proceedings of the ACM International Conference on Multimedia Retrieval 2014: April 1st-4th, 2014: Glasgow, UK
First page
33
Last page
40
Publisher
Association for Computing Machinery (ACM)
DOI
https://doi.org/10.1145/2578726.2578737 Open in new window
Project funding
PROCESADO DE ALTO RENDIMIENTO DE GRANDES CONJUNTOS DE DATOS REPRESENTADOS COMO GRAFOS
PTQ-11-04970 - Spin-off PYME Pequeña JEIs
Repository
http://hdl.handle.net/2117/23122 Open in new window
URL
http://dl.acm.org/citation.cfm?doid=2578726.2578737 Open in new window
Abstract
Annotation-based techniques for image retrieval suffer from sparse and short image textual descriptions. Moreover, users are often not able to describe their needs with the most appropriate keywords. This situation is a breeding ground for a vocabulary mismatch problem resulting in poor results in terms of retrieval precision. In this paper, we propose a query expansion technique for queries expressed as keywords and short natural language descriptions. We present a new massive query expansion s...
Citation
Guisado, J.; Dominguez, D.; Larriba, J. Massive query expansion by exploiting graph knowledge bases for image retrieval. A: ACM International Conference on Multimedia Retrieval. "Proceedings of the ACM International Conference on Multimedia Retrieval 2014: April 1st-4th, 2014: Glasgow, UK". Glasgow: Association for Computing Machinery (ACM), 2014, p. 33-40.
Keywords
Community detection, Graph mining techniques, Information retrieval, Knowledge bases, Query expansion, Wikipedia
Group of research
DAMA-UPC - Data Management Group

Participants