An Automatic Video Reinforcing System Based on Popularity Rating of Scenes and Level of Detail Controlling

Abstract

With the advance of video-on-demand (VOD) services such as Netfix, users are able to watch many kinds of videos anytime and anywhere. While watching a video, recently, users often search related information about it through the Web by using mobile PC. However, users cannot satisfactorily understand and enjoy it because the video keeps playing when they search about it. It is necessary to detect various questions of the video to supplement their related information about each scene for automatic search. However, only one video includes various topics of each scene, furthermore, viewers have different levels of knowledge. Therefore, we have developed a novel automatic video reinforcing system, called TV-Binder, it generates new video contents from one video stream related to viewers' interests and knowledge by adding other related contents (i.e., YouTube videos, images or maps) and by removing unnecessary original scenes, based on topics of each scene. As a result, viewers can satisfy and joyfully watch modified video contents without searching anything. At first, our system extract topics and detect their scenes of a video stream by using closed captions. The system then searches other necessary contents and determines unwanted original scenes based on popularity rating of each original scene and level of detail (LOD) controlling under time pressure. Through this, TV-Binder can automatically generate video contents are classified into four quadrants by two axes, one is digest and detailed videos, the other one is videos for experts with knowledge about particular topics and ordinary viewers without special knowledge. In this paper, we discuss our automatic video reinforcing system and an evaluation of its effectiveness.