Generating Rotation-Invariant Texture Features by Randomization of Operator Orientation

Rotation-invariant texture features are generated by randomizing the orientation of the underlying texture operators. The approach is applied to texture features based on local binary patterns as well as to sum and difference histograms. Results are given for a difficult classification problem of 15 different Brodatz textures and 7 rotation angles. Due to randomization, the error rate becomes independent of the texture orientation. Moreover, the classification of periodic textures is enhanced significantly.

[1]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[3]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[4]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[6]  Matti Pietikäinen,et al.  Rotation-invariant texture classification using feature distributions , 2000, Pattern Recognit..

[7]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[9]  Bedrich J. Hosticka,et al.  Unsupervised texture segmentation of images using tuned matched Gabor filters , 1995, IEEE Trans. Image Process..