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

Abstract

A new texture classification method based on wavelet transform is presented. The elements of the signature vector, FDBC, of an image are the fractal dimensions and barycentric coordinates of the bit planes of the wavelet coefficients in both the three-level high-frequency domains and the third low-frequency domain. The pretreatment is done with SVD decomposition and reconstruction by dropping half singular values. The one-nearest-neighbour classifier (1NN) with L 1 distance is used to make the classification. Furthermore, to improve classification result, the classifier 1NN is strengthened with weighted L 1 distance. The proposed method is tested on five subsets from Brodatz database and UMD database and is experimentally proved more efficient and more promising.