Video search for ambiguous requests
Ryunosuke Itabashi, Nobuyuki Yagi · 2020
The spread of broadband networks has resulted in the spread of countless videos on the internet. Advances in video analysis technology make it possible to extract more exact metadata and make it easier to find the video we want. However, search services are not suitable for ambiguous affective video searches in which a specific query cannot be given, such as whether you want to watch while relaxing. To solve this problem, this paper considers video search method that can handle such ambiguous requests by utilizing existing search services and propose a method of replacing ambiguous requests by queries that are a group of multiple metadata of something concrete such as names and situations of objects. This paper performs classification experiments with three ambiguous requests using multiple metadata automatically attached to the videos by Google Cloud Video Intelligence and so on, and confirms that automatic classification by machine learning performs close to manual classification. This suggests that the proposed method is feasible.