Local linear transforms for texture measurements
Signal Processing
Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation Using Voronoi Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Texture classification using invariant ranklet features
Pattern Recognition Letters
M-band ridgelet transform based texture classification
Pattern Recognition Letters
Journal of Visual Communication and Image Representation
Texture classification using combined image decomposition methods
Machine Graphics & Vision International Journal
Bayesian texture classification and retrieval based on multiscale feature vector
Pattern Recognition Letters
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
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Texture classification has long been an important research topic in image processing. Now a day's classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way compared to wavelet transform. In this paper, the issue of texture classification based on ridgelet transform has been analyzed. Features are derived from the sub-bands of the ridgelet decomposition and are used for classification for the four different datasets containing 20, 30, 112 and 129 texture images respectively. Experimental results show that this approach allows obtaining high degree of success rate in classification.