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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2009> 18(2): 186-191
TAN Li, CAO Yuan-da, YANG Ming-hua, HE Qiao-yan. Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2009, 18(2): 186-191.
Citation: TAN Li, CAO Yuan-da, YANG Ming-hua, HE Qiao-yan. Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2009, 18(2): 186-191.
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Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification

  • School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2008-07-11
    Abstract
  • To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine (PPSVM) is presented. A neighbor-based posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.
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    • References (11)
  • [1]
    Hauptmann A G. Lessons for the future from a decade of informedia video analysis research //Proc International Conference on Image and Video Retrieval. Singapore: Lecture Notes in Computer Science, 2005, 3568:1-10.
    [2]
    Smeaton A F, Over P, Kraaij W. Evaluation campaigns and TRECVid //Proc ACM International Workshop on Multimedia Information Retrieval. New York: ACM Press,2006:321-330.
    [3]
    Wang M, Hua X S, Yuan X, et al. Optimizing multi-graph learning: Towards a unified video annotation scheme //Proc ACM International Conference on Multimedia, New York: ACM Press,2007:862-871.
    [4]
    Wu Y, Chang Chang K C C, Chang E Y, et al. Optimal multimodal fusion for multimedia data analysis //Proc ACM International Conference on Multimedia. New York: ACM Press,2004:572-579.
    [5]
    Vapnik V N. The nature of statistical learning theory[M]. New York: Springer, 1999.
    [6]
    Akbani R, Stephen K, Japkowicz N. Applying support vector machines to imbalanced datasets //Proc European Conference on Machine Learning. Berlin: Springer, 2004, 3201:39-50.
    [7]
    Wu G, Chang E Y. Aligning boundary in kernel space for learning imbalanced dataset //Proc IEEE International Conference on Data Mining. : IEEE Computer Society, 2004: 265-272.
    [8]
    Cristianini N, Shawe-Taylor J, Elissee A, et al. On kernel target alignment[M]//Innovations in Machine Learning. New York: Springer,2004,194:205-256.
    [9]
    Tao Q, Wu G W, Wang F Y, et al. Posterior probability support vector machines for unbalanced data[J]. IEEE Transactions on Natural Networks, 2005,16(6):1561-1573.
    [10]
    Tang S, Zhang Y D, Li J T, et al. Trecvid 2007 high-level feature extraction by mcg-ict-cas //Proc TRECVID 2007. Gaithersburg, USA: TRECVID Workshop, 2007.
    [11]
    Lowe D. Distinctive image features from scale-invariant keypoints . International Journal on Computer Vision, 2004, 60(2):91-110. (Edited by
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