Clustering Web video search results based on integration of multiple features

  • Authors:

  • Alex Hindle;Jie Shao;Dan Lin;Jiaheng Lu;Rui Zhang

  • Affiliations:

  • Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Australia;Department of Computer Science, Missouri University of Science and Technology, Roulla, USA 65409;School of Information and DEKE, MOE, Renmin University of China, Beijing, People's Republic of China 100872;Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Australia

  • Venue:
  • World Wide Web
  • Year:
  • 2011

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Abstract

The usage of Web video search engines has been growing at an explosive rate. Due to the ambiguity of query terms and duplicate results, a good clustering of video search results is essential to enhance user experience as well as improve retrieval performance. Existing systems that cluster videos only consider the video content itself. This paper presents the first system that clusters Web video search results by fusing the evidences from a variety of information sources besides the video content such as title, tags and description. We propose a novel framework that can integrate multiple features and enable us to adopt existing clustering algorithms. We discuss our careful design of different components of the system and a number of implementation decisions to achieve high effectiveness and efficiency. A thorough user study shows that with an innovative interface showing the clustering output, our system delivers a much better presentation of search results and hence increases the usability of video search engines significantly.