Dergiler / Turkish Journal of Engineering and Environmental Sciences / 2001 / Cilt: 25 - Sayı: 5
Rotation-Invariant Texture Analysis and Classification by Artificial Neural Networks and Wavelet Transform
- Sayfa
- 405–413
- DOI
- —
Özet
A large number of approaches for texture analysis have been suggested for the purpose of texture classification. Recently, wavelet frames were proposed for texture features extraction. In this study, non-subsampled wavelet frame transform was used for feature extraction of 16 textures from a set of Brodatz' album by means of various wavelet families. Texture classification was accomplished by artificial neural network with a fast adaptive backpropagation algorithm. A new pyramidal-windowing algorithm is proposed, which forms randomly rotated texture windows of variable sizes texture windows for training a neural networks classifier, and perfect classification results were obtained.
Abstract
A large number of approaches for texture analysis have been suggested for the purpose of texture classification. Recently, wavelet frames were proposed for texture features extraction. In this study, non-subsampled wavelet frame transform was used for feature extraction of 16 textures from a set of Brodatz' album by means of various wavelet families. Texture classification was accomplished by artificial neural network with a fast adaptive backpropagation algorithm. A new pyramidal-windowing algorithm is proposed, which forms randomly rotated texture windows of variable sizes texture windows for training a neural networks classifier, and perfect classification results were obtained.