This is one, of many, possible ways of calculating a feature vector for a given textured image using the LS2W modelling framework. Please refer to Eckley et al. (2010) for details about the texture statistic being used. This function is only intended to be used with sample.stats.
sample.stats(x, n=25, size=64)A matrix containing the feature vectors for each sub-image. Each row contains the feature vector for one specific subimage
The textured image which is going to be analysed.
The number of sub-images to be sampled from the main texture.
The number of rows-columns required for each sub-image.
Idris Eckley
Eckley, I.A., Nason, G.P. and Treloar, R.L. (2010) Locally stationary wavelet fields with application to the modelling and analysis of image texture. Journal of the Royal Statistical Society (Series C), 59, 595 - 616.
Eckley, I.A. and Nason, G.P. (2011). LS2W: Implementing the Locally Stationary 2D Wavelet Process Approach in R, Journal of Statistical Software, 43(3), 1-23. URL http://www.jstatsoft.org/v43/i03/.
example.ls2w