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On the selection of an optimal wavelet basis for texture characterization
A Mojsilović1, M V Popović, D M Rackov
1IBM T. J. Watson Research Center, Hawthorne, NY 10532, USA. aleksand@us.ibm.com
Summary
Choosing the right wavelet filter bank significantly impacts texture characterization. This study identifies key filter properties for optimal wavelet-based texture analysis, ranking 19 filters.
Area of Science:
- Computer Vision
- Image Processing
- Signal Processing
Background:
- Wavelet-based algorithms are established for texture characterization.
- Filter bank selection criteria for texture analysis remain unclear, unlike in image coding.
Purpose of the Study:
- Investigate the role of decomposition filter properties in texture description.
- Determine dominant features for selecting optimal filter banks in wavelet-based texture analysis.
Main Methods:
- Performed texture classification experiments using 23 Brodatz textures.
- Evaluated the influence of different wavelet filter banks on texture characterization results.
Main Results:
- The choice of decomposition filters significantly affects texture characterization outcomes.
- Identified specific filter properties crucial for effective texture description.
Conclusions:
- Decomposition filter selection is critical for wavelet-based texture characterization.
- Established relevant criteria for choosing optimal filter banks for texture analysis.
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