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Emmanuel Dellandréa
Researcher at École centrale de Lyon
Publications - 106
Citations - 2332
Emmanuel Dellandréa is an academic researcher from École centrale de Lyon. The author has contributed to research in topics: Object detection & Audio signal. The author has an hindex of 25, co-authored 103 publications receiving 1864 citations. Previous affiliations of Emmanuel Dellandréa include University of Lyon & François Rabelais University.
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The MediaEval 2015 Affective Impact of Movies Task
Mats Sjöberg,Yoann Baveye,Hanli Wang,Vu Lam Quang,Bogdan Ionescu,Emmanuel Dellandréa,Markus Schedl,Claire-Hélène Demarty,Liming Chen +8 more
TL;DR: Insight is provided on the use case, task challenges, data set and ground truth, task run requirements and evaluation metrics of the MediaEval 2015 Aective Impact of Movies Task.
IRIM at TRECVID 2012: Semantic Indexing and Instance Search
Nicolas Ballas,Benjamin Labbé,Aymen Shabou,Hervé Le Borgne,Philippe-Henri Gosselin,Miriam Redi,Bernard Merialdo,Hervé Jégou,Jonathan Delhumeau,Remi Vieux,Boris Mansencal,Jenny Benois-Pineau,Stéphane Ayache,Abdelkader Hamadi,Bahjat Safadi,Franck Thollard,Nadia Derbas,Georges Quénot,Hervé Bredin,Matthieu Cord,Boyang Gao,Chao Zhu,Yuxing Tang,Emmanuel Dellandréa,Charles-Edmond Bichot,Liming Chen,Alexandre Benoit,Patrick Lambert,Tiberius Strat,Joseph Razik,Sébastien Paris,Hervé Glotin,Tran Ngoc Trung,D. Petrovska-Delacretaz,Gérard Chollet,Andrei Stoian,Michel Crucianu +36 more
TL;DR: The IRIM group is a consortium of French teams work- ing on Multimedia Indexing and Retrieval and its participation to the TRECVID 2011 se- mantic indexing and instance search tasks is described.
Proceedings ArticleDOI
A Large Video Data Base for Computational Models of Induced Emotion
TL;DR: The LIRIS-ACCEDE is an Annotated Creative Commons Emotional DatabasE composed of 9800 video clips extracted from 160 movies shared under Creative Commons licenses that allows to make this database publicly available without copyright issues.
Proceedings ArticleDOI
Features extraction and selection for emotional speech classification
TL;DR: A definition of emotions as 3-states emotions is also proposed in this paper, and a feature set of 50 potentially features is extracted and analyzed, and the best features are selected.