A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation invariant texture classification using even symmetric Gabor filters
Pattern Recognition Letters
Ranklets: Orientation Selective Non-Parametric Features Applied to Face Detection
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Rotation and scale invariant texture features using discrete wavelet packet transform
Pattern Recognition Letters
Robust Real-Time Face Detection
International Journal of Computer Vision
Texture classification using ridgelet transform
Pattern Recognition Letters
Texture Analysis Using Generalized Co-Occurrence Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
A ranklet-based CAD for digital mammography
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
IEEE Transactions on Image Processing
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
Fast algorithms for the computation of Ranklets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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A novel invariant texture classification method is proposed. Invariance to linear/non-linear monotonic gray-scale transformations is achieved by submitting the image under study to the ranklet transform, an image processing technique relying on the analysis of the relative rank of pixels rather than on their gray-scale value. Some texture features are then extracted from the ranklet images resulting from the application at different resolutions and orientations of the ranklet transform to the image. Invariance to 90^o-rotations is achieved by averaging, for each resolution, correspondent vertical, horizontal, and diagonal texture features. Finally, a texture class membership is assigned to the texture feature vector by using a support vector machine (SVM) classifier. Compared to three recent methods found in literature and having being evaluated on the same Brodatz and Vistex datasets, the proposed method performs better. Also, invariance to linear/non-linear monotonic gray-scale transformations and 90^o-rotations are evidenced by training the SVM classifier on texture feature vectors formed from the original images, then testing it on texture feature vectors formed from contrast-enhanced, gamma-corrected, histogram-equalized, and 90^o-rotated images.