TY - JOUR
T1 - QCompere @ REPERE 2013
AU - Bredin, Hervé
AU - Poignant, Johann
AU - Fortier, Guillaume
AU - Tapaswi, Makarand
AU - Le, Viet Bac
AU - Roy, Anindya
AU - Barras, Claude
AU - Rosset, Sophie
AU - Sarkar, Achintya
AU - Yang, Qian
AU - Gao, Hua
AU - Mignon, Alexis
AU - Verbeek, Jakob
AU - Besacier, Laurent
AU - Quénot, Georges
AU - Ekenel, Hazim Kemal
AU - Stiefelhagen, Rainer
N1 - Publisher Copyright:
Copyright © 2013 for the individual papers by the papers' authors.
PY - 2013
Y1 - 2013
N2 - We describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed.
AB - We describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed.
KW - Face recognition
KW - Multimodal fusion
KW - Named entity detection
KW - Speaker identification
KW - Video optical character recognition
UR - http://www.scopus.com/inward/record.url?scp=84903747023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903747023&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84903747023
SN - 1613-0073
VL - 1012
SP - 49
EP - 54
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 1st Workshop on Speech, Language and Audio in Multimedia, SLAM 2013
Y2 - 22 August 2013 through 23 August 2013
ER -