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Abstract

An automatic video tagging system which learns from videos, their web context and comments shared on social networks is described. Massive multimedia collections are analyzed by Internet crawling and a knowledge base is maintained 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 linked with other related contents. Practical applications of video recognition require a label scheme that is appealing to the end-user (i.e. obtained from social curation) and a training dataset that can be updated in real-time to be able to recognize new actions, scenes and people. To create this dataset that evolves in real-time and uses labels that are relevant to the users, a weakly-supervised deep learning approach is utilized combining both a machine-learning pre-processing stage together with a set of keywords obtained from the internet. The resulting tags combined with videos and summaries of videos are used with deep learning to train a neural network in an unsupervised manner that allows the tagging system to go from an image to a set of tags for the image and then to the visual representation of a tag.

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