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Semantic classifier based on compressed sensing for image and video annotation

Semantic classifier based on compressed sensing for image and video annotation

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A new semantic classification approach for image and video annotation is proposed, which fits a semantic classification task into theory of a compressed sensing framework. The proposed approach first utilises training samples to create a dictionary matrix and then uses a matching pursuit algorithm to find the sparse vector. The final annotations are determined according to the reconstruction value from the positive samples and the sparse vector. A systematic performance study on TRECVID 2008 video dataset and Corel image dataset shows the proposed approach is more effective than the traditional support vector machine scheme.

References

    1. 1)
      • TREC Video Retrieval Evaluation (TRECVID). http://www-nlpir.nist.gov/projects/trecvid/.
    2. 2)
    3. 3)
      • Vo Nhat, , Vo Duc, , Challa, S., Moran, B.: `Compressed sensing for face recognition', Presented at IEEE Symp. on Computational Intelligence for Image Processing, 2009, Nashville, TN, USA, p. 104–109, March–April.
    4. 4)
      • S. Mallat , Z. Zhang . Matching pursuit with time-frequency dictionaries. IEEE Trans. Signal Process. , 12 , 3397 - 3415
    5. 5)
      • C.C. Chang, C.J. Lin, ‘LIBSVM: a library for support vector machines’ (2001). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
    6. 6)
      • Aslam, J.A., Pavlu, V., Yilmaz, E.: `Statistical method for system evaluation using incomplete judgments', Proc. 29th ACM SIGIR Conf., 2006, Seattle, WA, USA.
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