Vector quantization and signal compression
Vector quantization and signal compression
Handbook of pattern recognition & computer vision
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multichannel watershed-based algorithm for supervised texture segmentation
Pattern Recognition Letters
Pyramid-based texture analysis/synthesis
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strong Markov Random Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Method of optimal directions for frame design
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Frame-theoretic analysis of oversampled filter banks
IEEE Transactions on Signal Processing
Fast orthogonal least squares algorithm for efficient subset modelselection
IEEE Transactions on Signal Processing
Frame representations for texture segmentation
IEEE Transactions on Image Processing
Texture segmentation using filters with optimized energy separation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Quantizing for minimum average misclassification risk
IEEE Transactions on Neural Networks
Digital Signal Processing
Journal of Mathematical Imaging and Vision
Non-negative sparse modeling of textures
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error, given a sparseness constraint, is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration, and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work, yielding excellent overall performance.