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

TL;DR

This paper explores the use of wavelet and curvelet moments as features for classifying images of aggregate mixtures, demonstrating improved effectiveness over traditional methods by capturing distributional behaviors.

Contribution

It introduces the use of higher-order moments of wavelet and curvelet coefficients for image classification of aggregate mixtures, enhancing feature representation.

Findings

Higher-order moments improve classification accuracy.

Wavelet and curvelet moments capture distributional characteristics.

Method outperforms traditional size and shape classification approaches.

Abstract

We show the potential for classifying images of mixtures of aggregate, based themselves on varying, albeit well-defined, sizes and shapes, in order to provide a far more effective approach compared to the classification of individual sizes and shapes. While a dominant (additive, stationary) Gaussian noise component in image data will ensure that wavelet coefficients are of Gaussian distribution, long tailed distributions (symptomatic, for example, of extreme values) may well hold in practice for wavelet coefficients. Energy (2nd order moment) has often been used for image characterization for image content-based retrieval, and higher order moments may be important also, not least for capturing long tailed distributional behavior. In this work, we assess 2nd, 3rd and 4th order moments of multiresolution transform -- wavelet and curvelet transform -- coefficients as features. As analysis methodology, taking account of image types, multiresolution transforms, and moments of coefficients in the scales or bands, we use correspondence analysis as well as k-nearest neighbors supervised classification.