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A New Video Feature Extraction Method Based on Local Class Information Preserving

  • Yongliang Xiao
  • Shaoping Zhu
  • Weizhong Luo
  • Xiangbao Li
  • Wenbin Liu
  • Gelan Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 132)

Abstract

Video feature extraction is the first step of video shot boundary detection. In this paper, a more useful and discriminating video feature extraction method based on local class information preserving is proposed. Maximum margin criterion is a very famous feature extraction method, which seeks to preserve global structure of samples, and can resolve small sample size problem. But this method ignores the local structure information of samples. To address the issue, we develop a new method namely local class information preserving (LCIP). We redefine the local between-class scatter matrix and within-class scatter matrix with the local structure information of each sample. Experimental results show the effectiveness of the proposed method.

Keywords

Shot boundary detection local structure information class information preserving 

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References

  1. 1.
    Li, Y.N., Lu, Z.M., Niu, X.M.: Fast Video Shot Boundary Detection Framework Employing Pre-Processing Techniques. IET Image Proc. 3(3), 121–134 (2009)MATHCrossRefGoogle Scholar
  2. 2.
    Camara-Chavez, G., Precioso, F., Cord, M.: Shot Boundary Detection by a Hierarchical Supervised Approach. In: 14th International Conference on Systems, Signals and Image Processing, pp. 197–200. IEEE Press, New York (2007)Google Scholar
  3. 3.
    Hansung, L., Jaehak, Y., Younghee, I., Joon-Min, G., Daihee, P.: A Unified Scheme of Shot Boundary Detection aAnd Anchor Shot Detection in News Video Story Parsing. Multimedia Tools Appl. 51(3), 1127–1145 (2011)CrossRefGoogle Scholar
  4. 4.
    Shekar, B.H., Sharmila Kumari, M., Holla, R.: Shot Boundary Detection Algorithm Based on Color Texture Moments. Commun. Comput. Info. Sci. 142(8), 591–594 (2011)CrossRefGoogle Scholar
  5. 5.
    Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annual of Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  6. 6.
    Li, H.F., Jiang, T., Zhang, K.S.: Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans. Neural Networks 17(1), 157–165 (2006)CrossRefGoogle Scholar
  7. 7.
    Yuan, J.H., Wang, H.Y., Xiao, L., Zheng, W.J., Li, J.M., Lin, F.Z., Zhang, B.: A Formal Study of Shot Boundary Detection. IEEE Trans. Circuits Syst. Video Technol. 17(2), 168–186 (2007)CrossRefGoogle Scholar
  8. 8.
    Zhang, H.J., Kankanhallli, A.S.: Automatic Partitioning of Video. Multimedia Syst. 1(1), 10–28 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yongliang Xiao
    • 1
  • Shaoping Zhu
    • 1
  • Weizhong Luo
    • 1
  • Xiangbao Li
    • 1
  • Wenbin Liu
    • 1
  • Gelan Yang
    • 2
  1. 1.Hunan University of Finance and EconomicChangshaChina
  2. 2.Hunan City UniversityChangshaChina

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