Result diversification in social image retrieval : a benchmarking framework

Ionescu, Bogdan (LAPI, University “Politehnica” of Bucharest, Bucharest 061071, Romania) ; Popescu, Adrian (CEA-LIST, Centre de Saclay - NanoInnov, Paris, France) ; Radu, Anca-Livia (LAPI, University “Politehnica” of Bucharest, Bucharest 061071, Romania / DISI, University of Trento, 38123 Povo, Italy) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

This article addresses the diversification of image retrieval results in the context of image retrieval from social media. It proposes a benchmarking framework together with an annotated dataset and discusses the results achieved during the related task run in the MediaEval 2013 benchmark. 38 multimedia diversification systems, varying from graph-based representations, re-ranking, optimization approaches, data clustering to hybrid approaches that included a human in the loop, and their results are described and analyzed in this text. A comparison of the use of expert vs. crowdsourcing annotations shows that crowdsourcing results have a slightly lower inter-rater agreement but results are comparable at a much lower cost than expert annotators. Multimodal approaches have best results in terms of cluster recall. Manual approaches can lead to high precision but often lower diversity. With this detailed results analysis we give future insights into diversity in image retrieval and also for preparing new evaluation campaigns in related areas.


Mots-clés:
Type d'article:
scientifique
Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Informatique
Date:
2015
Pagination:
31 p.
Veröffentlicht in:
Multimedia tools and applications
Numérotation (vol. no.):
octobre 2015, vol. 74, no 19
DOI:
ISSN:
1380-7501
Le document apparaît dans:

Note: The status of this file is: restricted


 Datensatz erzeugt am 2015-09-08, letzte Änderung am 2020-10-27

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