  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.
