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A Subspace Approach to Texture Modelling by Using Gaussian Mixtures

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4 Author(s)
Grim, J. ; Inst. of Inf. Theor. & Autom., Acad. of Sci. of the Czech Republic, Prague ; Haindl, M. ; Somol, P. ; Pudil, P.

Assuming local and shift-invariant texture properties we describe the statistical dependencies between pixels by a joint probability density of gray-levels within a suitably chosen observation window. We estimate the unknown multivariate density in the form of a Gaussian mixture of product components from data obtained by shifting the observation window. Obviously, the size of the window should be large to capture the low-frequency properties of textures but, on the other hand, the increasing dimension of the estimated mixture may become prohibitive. By considering a subspace approach based on a structural mixture model we can increase the size of the observation window while keeping the computational complexity in reasonable bounds

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Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:2 )

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