Difference between revisions of "Xrce's Participation in Wikipedia Retrieval, Medical Image Modality Classification and Ad-Hoc Retrieval Tasks of Imageclef 2010"
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| + | | title = Xrce's Participation in Wikipedia Retrieval, Medical Image Modality Classification and Ad-Hoc Retrieval Tasks of Imageclef 2010 | ||
| + | | date = 2010 | ||
| + | | authors = [[Stéphane Clinchant]]<br />[[Gabriela Csurka]]<br />[[Julien Ah-Pine]]<br />[[Guillaume Jacquet]]<br />[[Florent Perronnin]]<br />[[Jorge Sánchez]]<br />[[Keyvan Minoukadeh]] | ||
| + | | link = http://ceur-ws.org/Vol-1176/CLEF2010wn-ImageCLEF-ClinchantEt2010.pdf | ||
| + | }} | ||
'''Xrce's Participation in Wikipedia Retrieval, Medical Image Modality Classification and Ad-Hoc Retrieval Tasks of Imageclef 2010''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Stéphane Clinchant]], [[Gabriela Csurka]], [[Julien Ah-Pine]], [[Guillaume Jacquet]], [[Florent Perronnin]], [[Jorge Sánchez]] and [[Keyvan Minoukadeh]]. | '''Xrce's Participation in Wikipedia Retrieval, Medical Image Modality Classification and Ad-Hoc Retrieval Tasks of Imageclef 2010''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Stéphane Clinchant]], [[Gabriela Csurka]], [[Julien Ah-Pine]], [[Guillaume Jacquet]], [[Florent Perronnin]], [[Jorge Sánchez]] and [[Keyvan Minoukadeh]]. | ||
== Overview == | == Overview == | ||
This year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual Concept Detection and Annotation Task is presented in a separate paper. In this working note, authors rather focus on participation in the [[Wikipedia]] Retrieval Task and in two sub-tasks of the Medical Retrieval Task (Image Modality Classication and Ad-hoc Image Retrieval). Authors investigated mono-modal (textual and visual) and multi-modal retrieval and classication systems. For representing text authors used either standard language model or a power law (log-logistic or smoothed power law) distribution-based [[information retrieval]] model. For representing images, authors used Fisher Vectors improved by power and L2 normalizations and a spatial pyramid representation. With theses representations and simple linear classiers authors achieved | This year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual Concept Detection and Annotation Task is presented in a separate paper. In this working note, authors rather focus on participation in the [[Wikipedia]] Retrieval Task and in two sub-tasks of the Medical Retrieval Task (Image Modality Classication and Ad-hoc Image Retrieval). Authors investigated mono-modal (textual and visual) and multi-modal retrieval and classication systems. For representing text authors used either standard language model or a power law (log-logistic or smoothed power law) distribution-based [[information retrieval]] model. For representing images, authors used Fisher Vectors improved by power and L2 normalizations and a spatial pyramid representation. With theses representations and simple linear classiers authors achieved | ||
Revision as of 17:19, 13 February 2021
Authors | Stéphane Clinchant Gabriela Csurka Julien Ah-Pine Guillaume Jacquet Florent Perronnin Jorge Sánchez Keyvan Minoukadeh |
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Publication date | 2010 |
Links | Original |
Xrce's Participation in Wikipedia Retrieval, Medical Image Modality Classification and Ad-Hoc Retrieval Tasks of Imageclef 2010 - scientific work related to Wikipedia quality published in 2010, written by Stéphane Clinchant, Gabriela Csurka, Julien Ah-Pine, Guillaume Jacquet, Florent Perronnin, Jorge Sánchez and Keyvan Minoukadeh.
Overview
This year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual Concept Detection and Annotation Task is presented in a separate paper. In this working note, authors rather focus on participation in the Wikipedia Retrieval Task and in two sub-tasks of the Medical Retrieval Task (Image Modality Classication and Ad-hoc Image Retrieval). Authors investigated mono-modal (textual and visual) and multi-modal retrieval and classication systems. For representing text authors used either standard language model or a power law (log-logistic or smoothed power law) distribution-based information retrieval model. For representing images, authors used Fisher Vectors improved by power and L2 normalizations and a spatial pyramid representation. With theses representations and simple linear classiers authors achieved
