Mining Similarities for Clustering Web Video Clips
Shouqun Liu, Ming Bo Zhu, Quan Zheng · 2008
With the widespread use of online video application, the amount of online video clips becomes huge. Web video search engines can help users to locate video clips they are interested in. However, most video search engines return similar or near-duplicate videos together in the result lists, which is inconvenient for users to browse. This paper proposes a novel approach to cluster similar web searched videos based on video visual similarities mining. The visual information is extracted for each video clip at first, then the video clips are clustered according to the pair-wise similarities among them. To evaluate the effectiveness of the proposed method, experiments are conducted on YouTube video search results.