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.

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.

Abstract: A system and method is provided for generating summaries of video clips and then utilizing a source of data indicative of the consumption by viewers of those video summaries. In particular, summaries of videos are published and audience data is collected regarding the usage of those summaries, including which summaries are viewed, how they are viewed, the duration of viewing and how often. This usage information may be utilized in a variety of ways. In one embodiment, the usage information is fed into a machine learning algorithm that identifies, updates and optimizes groupings of related videos and scores of significant portions of those videos in order to improve the selection of the summary. In this way the usage information is used to find a summary that better engages the audience. In another embodiment usage information is used to predict popularity of videos. In still another embodiment usage information is used to assist in the display of advertising to users.

Abstract: A system and method is provided for generating summaries of video clips and then utilizing a source of data indicative of the consumption by viewers of those video summaries. In particular, summaries of videos are published and audience data is collected regarding the usage of those summaries, including which summaries are viewed, how they are viewed, the duration of viewing and how often. This usage information may be utilized in a variety of ways. In one embodiment, the usage information is fed into a machine learning algorithm that identifies, updates and optimizes groupings of related videos and scores of significant portions of those videos in order to improve the selection of the summary. In this way the usage information is used to find a summary that better engages the audience. In another embodiment usage information is used to predict popularity of videos. In still another embodiment usage information is used to assist in the display of advertising to users.