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Volume 28 Issue 5
September  2013
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Cheng-De Zhang, Xiao Wu, Mei-Ling Shyu, Qiang Peng. A Novel Web Video Event Mining Framework with the Integration of Correlation and Co-Occurrence Information[J]. Journal of Computer Science and Technology, 2013, 28(5): 788-796. DOI: 10.1007/s11390-013-1377-6
Citation: Cheng-De Zhang, Xiao Wu, Mei-Ling Shyu, Qiang Peng. A Novel Web Video Event Mining Framework with the Integration of Correlation and Co-Occurrence Information[J]. Journal of Computer Science and Technology, 2013, 28(5): 788-796. DOI: 10.1007/s11390-013-1377-6
shu

A Novel Web Video Event Mining Framework with the Integration of Correlation and Co-Occurrence Information

  • 1 School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;
    2 Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, U.S.A.

Funds: This work was supported by the National Natural Science Foundation of China under Grant Nos. 61373121, 61071184, 60972111, 61036008, the Research Funds for the Doctoral Program of Higher Education of China under Grant No. 20100184120009, the Program for Sichuan Provincial Science Fund for Distinguished Young Scholars under Grant Nos. 2012JQ0029, 13QNJJ0149, the Fundamental Research Funds for the Central Universities of China under Grant Nos. SWJTU09CX032, SWJTU10CX08, and the Program of China Scholarships Council under Grant No. 201207000050.
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  • Received Date: May 04, 2013
  • Revised Date: August 09, 2013
  • Published Date: September 04, 2013
    Abstract
  • The massive web videos prompt an imperative demand on effciently grasping the major events. However, the distinct characteristics of web videos, such as the limited number of features, the noisy text information, and the unavoidable error in near-duplicate keyframes (NDKs) detection, make web video event mining a challenging task. In this paper, we propose a novel four-stage framework to improve the performance of web video event mining. Data preprocessing is the first stage. Multiple Correspondence Analysis (MCA) is then applied to explore the correlation between terms and classes, targeting for bridging the gap between NDKs and high-level semantic concepts. Next, co-occurrence information is used to detect the similarity between NDKs and classes using the NDK-within-video information. Finally, both of them are integrated for web video event mining through negative NDK pruning and positive NDK enhancement. Moreover, both NDKs and terms with relatively low frequencies are treated as useful information in our experiments. Experimental results on large-scale web videos from YouTube demonstrate that the proposed framework outperforms several existing mining methods and obtains good results for web video event mining.
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