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Fundamentals of digital image processing
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Unsupervised texture segmentation using Gabor filters
Pattern Recognition
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Rotation Invariant Texture Features and Their Use in Automatic Script Identification
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Affine Invariant Texture Segmentation and Shape from Texture by Variational Methods
Journal of Mathematical Imaging and Vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Markov random field texture models
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International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
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Local descriptors and similarity measures for frontal face recognition: A comparative analysis
Journal of Visual Communication and Image Representation
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In this paper, we aim to handle the intraclass variation resulting from the geometric transformation and the illumination change for more robust texture classification. To this end, we propose a novel feature descriptor called Radon representation-based feature descriptor (RRFD). RRFD converts the original pixel represented images into Radon-pixel images by using the Radon transform. The new Radon-pixel representation is more informative in geometry and has a much lower dimension. Subsequently, RRFD efficiently achieves affine invariance by projecting an image (or an image patch) from the space of Radon-pixel pairs onto an invariant feature space by using a ratiogram, i.e., the histogram of ratios between the areas of triangle pairs. The illumination invariance is also achieved by defining an illumination invariant distance metric on the invariant feature space. Comparing to the existing Radon transform-based texture features, which only achieve rotation and/or scaling invariance, RRFD achieves affine invariance. The experimental results on CUReT show that RRFD is a powerful feature descriptor that is suitable for texture classification.