Texture classification and retrieval using shearlets and linear regression
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Cybernetics, 2015, 45 (3), pp. 358 - 369
- Issue Date:
- 2015-03-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| \\utsfs.adsroot.uts.edu.au\homes\staff\108848\Desktop\06853375.pdf | Published Version | 3.3 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2013 IEEE. Statistical modeling of wavelet subbands has frequently been used for image recognition and retrieval. However, traditional wavelets are unsuitable for use with images containing distributed discontinuities, such as edges. Shearlets are a newly developed extension of wavelets that are better suited to image characterization. Here, we propose novel texture classification and retrieval methods that model adjacent shearlet subband dependences using linear regression. For texture classification, we use two energy features to represent each shearlet subband in order to overcome the limitation that subband coefficients are complex numbers. Linear regression is used to model the features of adjacent subbands; the regression residuals are then used to define the distance from a test texture to a texture class. Texture retrieval consists of two processes: the first is based on statistics in contourlet domains, while the second is performed using a pseudo-feedback mechanism based on linear regression modeling of shearlet subband dependences. Comprehensive validation experiments performed on five large texture datasets reveal that the proposed classification and retrieval methods outperform the current state-of-the-art.
Please use this identifier to cite or link to this item: