Murtagh, Fionn and Starck, Jean-Luc (2008) Wavelet and curvelet moments for image classification: application to aggregate mixture grading. Pattern Recognition Letters, 29 (10).
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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.
This is a Published version This version's date is: 15/07/2008 This item is peer reviewed
https://repository.royalholloway.ac.uk/items/9ddb352c-101f-426f-4639-57f50b70d306/1/
Deposited by () on 23-Dec-2009 in Royal Holloway Research Online.Last modified on 23-Dec-2009
The eprint is the authors' final draft. Copyright 2008 Elsevier B.V.
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