Texture Classification by Bit-plane Multifractal Spectrum and Bit-plane Barycentric Coordinates of Wavelet Coefficients Based on SVD

Abstract

A new texture classification method based on singular value decomposition(SVD) and wavelet transform is presented. Wavelet transform is employed on texture images having been preprocessed with SVD. The elements of the signature vector of an image are the fractal dimensions and barycentric coordinates of the bit planes of the wavelet coefficients in both the 3-Level high frequency domains and the third low frequency domain. The one-nearest-neighbor classifier with standard L ଵ -norm distance is utilized to perform supervised texture classification. Compared with some other classification methods, the method is experimentally proved more efficient and less time-consuming.

Texture classification by multifractal spectrum and barycentric coordinates of bit planes of wavelet coefficients

“…To classify images with FDBC , the k NN classifier is utilised and standard ‐norm distance is used as the discrepancy measure [34]. Usually, minimum distance (MD) also is a widely used classifier.…”