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: According to one implementation, a video processing system for performing thumbnail generation includes a computing platform having a hardware processor and a system memory storing a thumbnail generator software code. The hardware processor executes the thumbnail generator software code to receive a video file, and identify a plurality of shots in the video file, each of the plurality of shots including a plurality of frames of the video file. For each of the plurality of shots, the hardware processor further executes the thumbnail generator software code to filter the plurality of frames to obtain a plurality of key frame candidates, determine a ranking of the plurality of key frame candidates based in part on a blur detection analysis and an image distribution analysis of each of the plurality of key frame candidates, and generate a thumbnail based on the ranking.

Abstract: According to one implementation, a video processing system for performing thumbnail generation includes a computing platform having a hardware processor and a system memory storing a thumbnail generator software code. The hardware processor executes the thumbnail generator software code to receive a video file, and identify a plurality of shots in the video file, each of the plurality of shots including a plurality of frames of the video file.