Efficient Video Indexing on the Web: A System that Leverages User Interactions with a Video Player
This work addresses efficient video indexing for web platforms, offering a complementary approach to content-based methods, though it is incremental with limited user testing.
The paper tackles the problem of generating representative thumbnails for online videos by analyzing user interactions like pauses and skips, resulting in the VideoSkip system that indexes content based on implicit feedback from nine users.
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.