Grant: Applied Research and Development Grants - Level 1
Seneca College of Applied Arts and Technology
$24,940.00
Advancing video categorization
EF 2018-19
Résumé
Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of*algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble has*experienced significant year over year growth since incorporation, with an expanding customer base in the education and media*spaces. They have spent several years building their technology and confirming their product market fit with early customers and*are ready to scale. Through this project, Vubble with collaborate with Dr. Vida Movahedi from Senecas School of Information and*Communications Technology to develop a machine-learning algorithm that will automatically output highly probable categories for*videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to*better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This*video identification research project will be Vubbles first step in understanding how to automate the identification of accurate*video. The need for automation of videos curation is prevalent, as video is quickly becoming the worlds dominant form of media*consumption, particularly for digital-native younger audiences. Furthermore, the results of the applied research will aid Vubble in*moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake*news video created from archival footage.
Programme
Applied Research and Development Grants - Level 1
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
522683-2017
Name nom
Williamson, Vanessa
Department département
Markham Campus
Organization id
1803
Institution établissement
Seneca College of Applied Arts and Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2017
Award amount
24940
Program id
CARD1
Program name en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
2713
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Algorithms
Research subject fr
Algorithmes
Partie
1
Nb partie
1
Application title
Advancing video categorization
Keyword
NSERC
Application summary
Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of*algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble has*experienced significant year over year growth since incorporation, with an expanding customer base in the education and media*spaces. They have spent several years building their technology and confirming their product market fit with early customers and*are ready to scale. Through this project, Vubble with collaborate with Dr. Vida Movahedi from Senecas School of Information and*Communications Technology to develop a machine-learning algorithm that will automatically output highly probable categories for*videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to*better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This*video identification research project will be Vubbles first step in understanding how to automate the identification of accurate*video. The need for automation of videos curation is prevalent, as video is quickly becoming the worlds dominant form of media*consumption, particularly for digital-native younger audiences. Furthermore, the results of the applied research will aid Vubble in*moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake*news video created from archival footage.