Classification Model Analysis of Logging Image Based on Metafeatures

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 143)

Abstract

In this paper, we proposed a classified strategy on borehole images of logging interpretation. According to the visualized characteristics of borehole images, the analysis of texture is the main objective of this paper. Local Binary pattern (LBP) histogram is adopted to capture the texture information that will be defined as low level textures. Then dominant features of a certain kind will be figured out among low level textures. Finally, after arrangement combination of these dominant features, appropriate meta feature will be presented. The meta feature is a certain kind of feature structure that used as input vectors for classifier.

Keywords

Metafeatures LBP Algorithm Texture analysis and feature extraction Bore Image analysis 

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References

  1. 1.
    Wang, F., Wen, Z.F., Zhong, J.H., Li, Y., Wang, H.Q.: Logging response and interpretation on reef in Western Qaidam Basin, vol. 29(2), pp. 133–136. Well Logging Technology, China (2005)Google Scholar
  2. 2.
    Goyal, R.K., Goh, W.L., Mital, D.P., Chan, K.L.: Invariant element compactness for texture classification. In: Proceedings of the Third International Conference on Automation, Robotics and Computer Vision (1994)Google Scholar
  3. 3.
    Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern Recognition 35, 735–747 (2002)MATHCrossRefGoogle Scholar
  4. 4.
    Carlos, A.-C., Risto, R.-K., Mario, R.-A., Andrade-Gonzalez, A., Rafael, E.-P.: High-order statistical texture analysis-—font recognition applied. Pattern Recognition Letters 26(2) (January 15, 2005)Google Scholar
  5. 5.
    Julesz, B.: Experiments in the visual perception of texture. Sci. Am. 232, 34–43 (1975)CrossRefGoogle Scholar
  6. 6.
    Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: Lvqpak: A software package for the correct application of learning vector quantization algorithms (1992), http://ieeexplore.ieee.org

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringBUAABeijingChina
  2. 2.School of SoftwareBUAABeijingChina

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