Efficient Video Indexing on the Web : A System that Crowdsources User Interactions with a Video Player
In this paper, we propose a user-based video-indexing method, that automatically generates thumbnails of the
most important scenes of an online video stream, by analyzing users’ interactions with a web video player. As a test bench to
verify our idea we have extended the YouTube video player into the VideoSkip system. In addition, VideoSkip uses a web-database
(Google Application Engine) to keep a record of some important parameters, such as the timing of basic user actions (play,
pause, skip). Moreover, we implemented an algorithm that selects representative thumbnails. Finally, we populated the system
with data from an experiment with nine users. We found that the VideoSkip system indexes video content by leveraging implicit
users interactions, such as pause and thirty seconds skip. Our early findings point toward improvements of the web video player
and its thumbnail generation technique. The VideSkip system could compliment content-based algorithms, in order to achieve
efficient video-indexing in difficult videos, such as lectures or sports.
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DOI
Leftheriotis, I., Gkonela, C., and Chorianopoulos, K. 2012. Efficient Video Indexing on the Web : A System that Crowdsources User Interactions with a Video Player. 2nd International ICST Conference on User Centric Media (UCMEDIA 2010), Springer Berlin Heidelberg, 123–131.BibTeX
Leftheriotis, I., Gkonela, C., and Chorianopoulos, K. 2012. Efficient Video Indexing on the Web : A System that Crowdsources User Interactions with a Video Player. 2nd International ICST Conference on User Centric Media (UCMEDIA 2010), Springer Berlin Heidelberg, 123–131.