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Wald-Wolfowitz Runs Test II01:17

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Statistical wavelet subband characterization based on generalized gamma density and its application in texture

S K Choy1, C S Tong

  • 1Department of Mathematics, Hong Kong BaptistUniversity, Kowloon Tong, Hong Kong. skchoy@math.hkbu.edu.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 1, 2009
PubMed
Summary

This study introduces the generalized Gamma density (GGammaD) for enhanced image analysis and texture retrieval. GGammaD offers superior flexibility over GGD, improving histogram modeling and image database searching.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Statistical distributions are crucial for modeling image data in various applications.
  • Existing methods like the generalized Gaussian density (GGD) have limitations in capturing complex histogram shapes.

Purpose of the Study:

  • To propose the three-parameter generalized Gamma density (GGammaD) for modeling wavelet detail subband histograms.
  • To apply GGammaD for efficient texture image retrieval.
  • To introduce a flexible parametric model for image data analysis.

Main Methods:

  • Utilizing the three-parameter generalized Gamma density (GGammaD) for histogram modeling.
  • Employing the symmetrized Kullback-Leibler distance (SKLD) to measure discrepancies between GGammaDs.
  • Deriving a closed-form solution for SKLD between GGammaDs for computational efficiency.

Main Results:

  • GGammaD demonstrates greater flexibility in shape control compared to GGD, crucial for histogram-based applications.
  • A closed-form SKLD for GGammaDs allows direct and effective distance computation using model parameters.
  • The proposed method shows superior performance in texture image retrieval on benchmark databases.

Conclusions:

  • The generalized Gamma density (GGammaD) provides a more flexible and effective model for wavelet subband histograms.
  • The derived closed-form SKLD facilitates efficient image retrieval from large databases.
  • The proposed GGammaD-based approach outperforms existing methods in texture image retrieval tasks.