A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Model-based texture segmentation and classification
Handbook of pattern recognition & computer vision
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation and scale invariant texture features using discrete wavelet packet transform
Pattern Recognition Letters
A translation- and scale-invariant adaptive wavelet transform
IEEE Transactions on Image Processing
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Scaling-invariant boundary image matching using time-series matching techniques
Data & Knowledge Engineering
A novel secure image hashing based on reversible watermarking for forensic analysis
ARES'11 Proceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems
Journal of Visual Communication and Image Representation
Continuous rotation invariant local descriptors for texton dictionary-based texture classification
Computer Vision and Image Understanding
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In this paper, we propose a rotation and scaling invariant feature set based on Radon transform and multiscale analysis. Radon transform is used to project the image to 1-D space, and then the rows of the projection matrix are transformed by an adaptive 1-D wavelet transform, thus the feature matrix with scaling invariance is derived in the Radon-wavelet domain. Multiscale analysis is employed for the feature matrix, and the energy values at different scales are proven not only to be invariant under image scaling and rotation, but also to reflect the different energy distributions of the texture image at different scales. In the classification stage, Mahalanobis classifier is used to classify 25 classes of distinct natural textures. Using the testing image sets with different orientations and scaling, experimental results show that the average recognition rate for joint rotation and scaling invariance of our proposed classification method can be 92.2%.