How many clusters are best?—an experiment
Pattern Recognition
Unsupervised textured image segmentation using feature smoothing probabilistic relaxation techniques
Computer Vision, Graphics, and Image Processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texture classification using the cortex transform
CVGIP: Graphical Models and Image Processing
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
Image coding using wavelet transform
IEEE Transactions on Image Processing
Wavelet-based phase classification
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Indoor versus outdoor scene classification using probabilistic neural network
EURASIP Journal on Applied Signal Processing
Texture description using different wavelet transforms based on statistical parameters
WAV'08 Proceedings of the 2nd WSEAS International Conference on Wavelets Theory and Applications in Applied Mathematics, Signal Processing and Modern Science
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This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.