Local Linear Transforms for Texture Measurements
Signal Processing, vol. 11, no. 1, pp. 61-79, July 1986.
The Nth order probability density function for pixels in a restricted neighborhood may be characterized by a set of N histograms (or some corresponding moments) computed along appropriately chosen axes. The projections on those axes are obtained from a local linear transform of the local neighborhood vector. This approach is closely related to filter bank analysis methods and gives a statistical justification for the extraction of texture properties by means of convolution operators or local matches. Optimal and sub-optimal linear operators are derived for texture analysis and classification. Experimental results indicate that the method is robust, flexible, and that it performs as well as standard co-occurrence based methods for texture classification. The proposed approach enables texture characterization with a lower number of features and it is also computationally more appealing.
TITLE="Local Linear Transforms for Texture Measurements",
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