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Invariant texture classification using a spatial filter bank in multi-resolution analysis
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DOI：10.1016/j.imavis.2015.10.002.png)
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This paper proposed a new method based on spatial filter banks and discrete wavelet transform (DWT) for invariant texture classification. The method used a multi-resolution analysis method like DWT and applied the proposed filter bank on different resolutions. Then, a simple fusion of features on different resolutions was used for invariant texture analysis. A comprehensive study was done to examine the effectiveness of the proposed method. Different datasets with different properties were used in this paper such as Brodatz, Outex, and KTH-TIPS for the evaluation. Local binary pattern (LBP) methods have been one of the powerful methods in recent years for invariant texture classification. A comparative study was performed with some state-of-the-art LBP methods. This comparison indicated promising results for the proposed approach as compared with the LBP methods. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Texture classification
Multi-resolution analysis
Wavelet transform
Spatial filter bank
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