Mining Similarities for Clustering Web Video Clips

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.

[1]  Avideh Zakhor,et al.  Fast similarity search and clustering of video sequences on the world-wide-web , 2005, IEEE Transactions on Multimedia.

[2]  Yixin Chen,et al.  CLUE: cluster-based retrieval of images by unsupervised learning , 2005, IEEE Transactions on Image Processing.

[3]  Trevor Darrell,et al.  Efficient image matching with distributions of local invariant features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Xian-Sheng Hua,et al.  Multi-modality web video categorization , 2007, MIR '07.

[5]  Chong-Wah Ngo,et al.  Practical elimination of near-duplicates from web video search , 2007, ACM Multimedia.

[6]  Justin Zobel,et al.  Clustering near-duplicate images in large collections , 2007, MIR '07.

[7]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.