MIRACLE (FI) at ImageCLEFphoto 2009

Granados Muñoz, Rubén and Benavent, Xaro and Aguerri, Rodrigo and García Serrano, Ana and Goñi Menoyo, José Miguel and Gomar, J. and Ves, Esther de and Domingo, J. and Ayala, G. (2009). MIRACLE (FI) at ImageCLEFphoto 2009. In: "10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009", 30/09/2008-02/10/2008, Corfu, Grecia. ISBN 978-88-88506-84-5.


Title: MIRACLE (FI) at ImageCLEFphoto 2009
  • Granados Muñoz, Rubén
  • Benavent, Xaro
  • Aguerri, Rodrigo
  • García Serrano, Ana
  • Goñi Menoyo, José Miguel
  • Gomar, J.
  • Ves, Esther de
  • Domingo, J.
  • Ayala, G.
Item Type: Presentation at Congress or Conference (Article)
Event Title: 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009
Event Dates: 30/09/2008-02/10/2008
Event Location: Corfu, Grecia
Title of Book: Working Notes for the CLEF 2009 Workshop
Date: 2009
ISBN: 978-88-88506-84-5
Freetext Keywords: Information Retrieval, Content-Based Image Retrieval, Merged result lists, Indexing, Named Entities Recognition
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Matemática Aplicada a las Tecnologías de la Información [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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The Miracle-FI participation at ImageCLEF 2009 photo retrieval task main goal was to improve the merge of content-based and text-based techniques in our experiments. The global system includes our own implemented tool IDRA (InDexing and Retrieving Automatically), and the Valencia University CBIR system. Analyzing both “topics_part1.txt” and “topics_part2.txt” task topics files, we have built different queries files, eliminating the negative sentences with the text from title and clusterTitle or clusterDescription, one query for each cluster (or not) of each topic from 1 to 25 and one for each of the three images of each topic from 26 to 50. In the CBIR system the number of low-level features has been increased from the 68 component used at ImageCLEF 2008 up to 114 components, and in this edition only the Mahalanobis distance has been used in our experiments. Three different merging algorithms were developed in order to fuse together different results lists from visual or textual modules, different textual indexations, or cluster level results into a unique topic level results list. For the five runs submitted we observe that MirFI1, MirFI2 and MifFI3 obtain quite higher precision values than the average ones. Experiment MirFI1, our best run for precision metrics (very similar to MirFI2 and MirFI3), appears in the 16th position in R-Precision classification and in the 19th in MAP one (from a total of 84 submitted experiments). MirFI4 and MirFI5 obtain our best diversity values, appearing in position 11th (over 84) in cluster recall classification, and being the 5th best group from all the 19 participating ones.

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Item ID: 4661
DC Identifier: http://oa.upm.es/4661/
OAI Identifier: oai:oa.upm.es:4661
Deposited by: Memoria Investigacion
Deposited on: 21 Oct 2010 11:47
Last Modified: 20 Apr 2016 13:47
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