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Texture segmentation is an important step in image analysis.
While human perception has great capacity in texture
segmentationand recognition, it is difficult to segment texture
images automatically by computers.Hence, we are motivated to
apply advanced statistical tools together withthe vision model
to mimic human perception.It is aimed to extract the features of
textures for further segmentationand recognition based on these
techniques.Different transforms ranging from the space domain,
the Fourier transform in the frequency domain, and the Gabor
filter banks in the space-frequencyanalysis are considered to
generate the feature vectors of textures.Dimension reduction
techniques are used to find out the projecteddirections of
feature vectors.Based on these projected feature vectors,
classification rules are selected by the sliced inverse
regression when the training set isavailable.For unsupervised
segmentation, initial clustering is suggested.Then, the
technique of sliced inverse regression is applied iteratively to
recluster the texture image by adjusting the projected feature
vectorsdynamically.The simulation and empirical studies
demonstrated the feasibility ofthese new approaches.
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