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We show the potential for classifying images of mixtures of aggregate, basedthemselves on varying, albeit well-defined, sizes and shapes, in order toprovide a far more effective approach compared to the classification ofindividual sizes and shapes. While a dominant (additive, stationary) Gaussiannoise component in image data will ensure that wavelet coefficients are ofGaussian distribution, long tailed distributions (symptomatic, for example, ofextreme values) may well hold in practice for wavelet coefficients. Energy (2ndorder moment) has often been used for image characterization for imagecontent-based retrieval, and higher order moments may be important also, notleast for capturing long tailed distributional behavior. In this work, weassess 2nd, 3rd and 4th order moments of multiresolution transform -- waveletand curvelet transform -- coefficients as features. As analysis methodology,taking account of image types, multiresolution transforms, and moments ofcoefficients in the scales or bands, we use correspondence analysis as well ask-nearest neighbors supervised classification.

Wavelet and Curvelet Moments for Image Classification: Application to Aggregate Mixture Grading