腾讯 AI Lab 官网

腾讯 AI Lab 官网
Hierarchical Visualization of Video Search Results for Topic-based Browsing
Abstract View the Paper
Existing video search engines return a ranked list of videos for each user query, which is not convenient for browsing the results of query topics that have multiple facets, such as the “early life,” “personal life,” and “presidency” of a query “Barack Obama.” Organizing video search results into semantically structured hierarchies with nodes covering different topic facets can significantly improve the browsing efficiency for such queries. In this paper, we introduce a hierarchical visualization approach for video search result browsing, which can help users quickly understand the multiple facets of a query topic in a very well-organized manner. Given a query, our approach starts from the hierarchy of its textual descriptions normally available on Wikipedia and then adjusts the hierarchical structure by analyzing the video information to reflect the topic structure of the search result. After that, a simple optimization problem is formulated to perform the video-to-node association considering three important criteria. Furthermore, additional topic facets are mined to complement the contents of the existing semantic hierarchies. A large YouTube video dataset is constructed to evaluate our approach both quantitatively and qualitatively. A demo system is also developed for users to interact with the proposed browsing approach.
Venue
IEEE Transactions on Multimedia, vol. 18, no. 11, pp. 2161-2170, Sep.
Publication Time
2016
Authors
Yu-Gang Jiang, Jiajun Wang, Qiang Wang, Wei Liu, and Chong-Wah Ngo