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Texture analysis and classification with tree-structured wavelet transform.

T Chang1, C J Kuo

  • 1Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1993
PubMed
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This study introduces a tree-structured wavelet transform for analyzing textures. This novel approach offers improved texture classification by focusing on middle frequency channels, outperforming existing methods.

Area of Science:

  • Signal Processing
  • Computer Vision
  • Image Analysis

Background:

  • Natural textures often exhibit quasi-periodic signal characteristics.
  • Dominant frequencies in many textures reside in middle frequency channels.
  • Conventional wavelet transforms primarily decompose low-frequency channels.

Purpose of the Study:

  • To propose a modified wavelet transform for enhanced texture analysis.
  • To develop a progressive texture classification algorithm with high computational efficiency and performance.
  • To address limitations of conventional wavelet transforms in texture analysis.

Main Methods:

  • Utilized a tree-structured wavelet transform (wavelet packets) for multiresolution analysis.
  • Enabled zooming into specific frequency channels for targeted decomposition.

Related Experiment Videos

  • Developed a progressive algorithm for texture classification.

    • Main Results:

    • The proposed transform allows detailed analysis of middle frequency channels.
    • The developed texture classification algorithm demonstrates excellent performance.
    • The method is computationally attractive compared to other techniques.

    Conclusions:

    • The tree-structured wavelet transform is effective for texture analysis, particularly for quasi-periodic signals.
    • This approach offers advantages over conventional pyramid-structured wavelet transforms.
    • The progressive classification algorithm provides an efficient and high-performing solution for texture recognition.