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Multiple-View Constrained Clustering For ...
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Document type :
Communication dans un congrès avec actes
DOI :
10.1109/ICASSP.2014.6853645
Title :
Multiple-View Constrained Clustering For Unsupervised Face Identification In TV-Broadcast
Author(s) :
Bendris, Meriem [Auteur]
Laboratoire d'informatique Fondamentale de Marseille [LIF]
Charlet, Delphine [Auteur]
France Télécom Recherche & Développement [FT R&D]
Favre, Benoit [Auteur]
Traitement Automatique du Langage Ecrit et Parlé [TALEP]
Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 [LIF]
Damnati, Géraldine [Auteur]
France Télécom Recherche et Développement [Lannion] [FTR&D]
Auguste, Rémi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
FOX MIIRE [LIFL]
Conference title :
ICASSP2014 - Image, Video, and Multidimensional Signal Processing (ICASSP2014 - IVMSP)
City :
Florence
Country :
Italie
Start date of the conference :
2014-05-04
Publication date :
2014
HAL domain(s) :
Informatique [cs]/Informatique et langage [cs.CL]
English abstract : [en]
Our goal is to automatically identify faces in TV broadcast without a pre-defined dictionary of identities. Most methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual ...
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Our goal is to automatically identify faces in TV broadcast without a pre-defined dictionary of identities. Most methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clustering. In TV content, people appear with many variations making the clustering difficult. In this case, speaker clustering can be a reliable link for face clustering. Multi-modal clustering methods assume a bipartite mapping between modalities. In this paper, we propose to build automatically an incomplete speaker-face mapping based on local evidence of OCR and Lip activity links. Then, we propose schemes of speaker constraints propagation to the face constrained-clustering problem. Experiments performed on the REPERE corpus show an improvement of face identification by propagating names to face clusters (+3.7% F-measure compared to the baseline).Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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  • http://pageperso.lif.univ-mrs.fr/~benoit.favre/papers/favre_icassp2014c.pdf
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