Title:
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Towards large scale multimedia indexing: a case study on person discovery in broadcast news
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Author:
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Le, Nam; Bredin, Hervé; Sargent, Gabriel; India, Miquel; López Otero, Paula; Barras, Claude; Guinaudeau, Camille; Gravier, Guillaume; Barbosa da Fonseca, Gabriel; Lyon Freire, Izabela; Patrocínio, Zenilton; Guimarães, Silvio Jamil F.; Martí Juan, Gerard; Morros, Josep Ramon; Hernando, Javier; Docio Fernández, Laura; García Mateo, Carmen; Meignier, Sylvain; Odobez, Jean-Marc
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Abstract:
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Comunicació presentada a: the 15th International Workshop on Content-Based Multimedia Indexing (CBMI'17), celebrat a Florència, Itàlia, del 19 al 21 de juny de 2017 |
Abstract:
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The rapid growth of multimedia databases and the human interest
in their peers make indices representing the location and identity
of people in audio-visual documents essential for searching
archives. Person discovery in the absence of prior identity knowledge
requires accurate association of audio-visual cues and detected
names. To this end, we present 3 different strategies to approach
this problem: clustering-based naming, verification-based naming,
and graph-based naming. Each of these strategies utilizes different
recent advances in unsupervised face / speech representation, verification,
and optimization. To have a better understanding of the
approaches, this paper also provides a quantitative and qualitative
comparative study of these approaches using the associated corpus
of the Person Discovery challenge at MediaEval 2016. From the
results of our experiments, we can observe the pros and cons of
each approach, thus paving the way for future promising research
directions. |
Abstract:
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This work was supported by the EU project EUMSSI (FP7-611057), ANR project MetaDaTV (ANR-14-CE24-0024) project, Camomile project (PCIN-2013-067), and the projects TEC2013-43935-R, TEC2015-69266-P, TEC2016-75976-R, TEC2015-65345-P financed by the Spanish government and ERDF. |
Rights:
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© 2017 Association for Computing Machinery
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Document type:
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Conference Object Article - Accepted version |
Published by:
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ACM Association for Computer Machinery
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