Organizing video search results to adapted semantic hierarchies for topic-based browsing

Organizing video search results into semantically structured hierarchies can greatly improve the efficiency of browsing complex query topics. Traditional hierarchical clustering techniques are inadequate since they lack the ability to generate semantically interpretable structures. In this paper, we introduce an approach to organize video search results to an adapted semantic hierarchy. As many hot search topics such as celebrities and famous cities have Wikipedia pages where hierarchical topic structures are available, we start from the Wikipedia hierarchies and adjust the structures according to the characteristics of the returned videos from a search engine. Ordinary clustering based on textual information of the videos is performed to discover the hidden topic structures in the video search results, which are used to adapt the hierarchy extracted from Wikipedia. After that, a simple optimization problem is formulated to assign the videos to each node of the hierarchy considering three important criteria. Experiments conducted on a Youtube video dataset verify the effectiveness of our approach.