ViTS: Video Tagging System from Massive Web Multimedia Collections

Delia Fernandez, David Varas, Joan Espadaler, Issey Masuda, Jordi Ferreira, Alejandro Woodward, David Rodriguez, Xavier Giro-i-Nieto, Juan Carlos Riveiro, Elisenda Bou
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
The popularization of multimedia content on the Web has arised the need to automatically understand, index and retrieve it. In this paper we present ViTS, an automatic Video Tagging System which learns from videos, their web context and comments shared on social networks. ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and
more » ... ked with other related contents. ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month. We compare the quality and completeness of our tags with respect to the ones in the YouTube-8M dataset, and we show how ViTS enhances the semantic annotation of the videos with a larger number of labels (10.04 tags/video), with an accuracy of 80,87%. Extracted tags and video summaries are publicly available. 1
doi:10.1109/iccvw.2017.48 dblp:conf/iccvw/FernandezVEMFWR17 fatcat:j4crsduvbjgrxe6rjztws7vsme