Title :
Statistical texture characterization from discrete wavelet representations
Author :
Van de Wouwer, G. ; Scheunders, P. ; Van Dyck, D.
Author_Institution :
Dept. of Phys., Antwerp Univ., Belgium
fDate :
4/1/1999 12:00:00 AM
Abstract :
We conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets: (1) the wavelet histogram signatures which capture all first order statistics using a model based approach and (2) the wavelet co-occurrence signatures, which reflect the coefficients´ second-order statistics. The introduced feature sets outperform the traditionally used energy. Best performance is achieved by combining histogram and co-occurrence signatures
Keywords :
discrete wavelet transforms; feature extraction; image classification; image representation; image texture; statistical analysis; discrete wavelet representations; feature sets; first order statistics; image texture; model based approach; second-order statistics; statistical texture characterization; wavelet co-occurrence signatures; wavelet detail coefficients; wavelet histogram signatures; Discrete wavelet transforms; Energy resolution; Feature extraction; Histograms; Image analysis; Image resolution; Image segmentation; Image texture analysis; Statistics; Wavelet analysis;
Journal_Title :
Image Processing, IEEE Transactions on
