Master 2015 2016
Stages de la spécialité SAR
Multimodal-based Interestingness Prediction


Site : Technicolor R&I
Lieu : Technicolor is an industry leader in the production of video content for movies, TV, advertising, games and more. The company provides production, postproduction, and distribution services to content creators, network service providers and broadcasters. Technicolor Research Rennes is the largest Technicolor Research Center conducting research in various domains applying to the creation, management and delivery of digital content. For more information on Technicolor R&I Rennes: http://www.technicolor.com/en/innovation/research-innovation/ri-laboratories/ The internship will be hosted in Imaging Science Lab (ISL) within Technicolor R&I Rennes. More specifically, he/she will join the “Contextual Media” team composing of more than twelve researchers and engineers coming from many different countries. One major goal of the team is to understand, organize, and enhance content, both professional and user-generated.
Encadrant : Claire-Hélène Demarty, Ngoc Duong, Frédéric Lefebvre and Alexey Ozerov E-mail : claire-helene.demarty@technicolor.com / quang-khanh-ngoc.duong@technicolor.com / frederic.lefebvre@technicolor.com / alexey.ozerov@technicolor.com If interested, please apply as described here: http://www.technicolor.com/en/innovation/student-day/job-internship-opportunities-ri-labs/video-processing-internships
Dates :du 01/02/2016 au 01/08/2016
Rémunération :1200 euros / month (brut)
Mots-clés : Parcours ATIAM : Acoustique, Parcours ATIAM : Informatique musicale, Parcours ATIAM : Traitement du signal

Description

Knowing whether a media content, i.e., image or video, is interesting for a viewer has numerous applications from assets management, improved education, to targeted advertising. This internship proposal targets the development and implementation of such an interestingness prediction algorithm based on machine learning techniques. One expected output is to submit a system to the 2016 MediaEval (http://www.mutimediaeval.org) task on interestingness.