Efficient Video Indexing on the Web: A System that Leverages User Interactions with a Video Player

TL;DR

This paper introduces VideoSkip, a user-interaction-based video indexing system that automatically generates scene thumbnails by analyzing user actions, enhancing video navigation especially for complex content.

Contribution

The paper presents a novel method leveraging user interactions for automatic video thumbnail generation, extending YouTube's player with a new indexing system and an algorithm for selecting representative thumbnails.

Findings

VideoSkip effectively indexes videos using implicit user interactions.

The system improves thumbnail relevance for complex videos.

Early results suggest potential for enhancing video navigation.

Abstract

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.