Statistical texture characterization from discrete wavelet representations

1 April 1999

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing

Vol. 8 (4) , 592-598
https://doi.org/10.1109/83.753747

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

This publication has 25 references indexed in Scilit:

Transform features for texture classification and discrimination in large image databases
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2002
Wavelet correlation signatures for color texture characterization
Pattern Recognition, 1999
Multi-resolutional gabor filter in texture analysis
Pattern Recognition Letters, 1996
Frame representations for texture segmentation
IEEE Transactions on Image Processing, 1996
Texture features for browsing and retrieval of image data
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1996
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing, 1993
Texture classification using QMF bank-based subband decomposition
CVGIP: Graphical Models and Image Processing, 1992
Ten Lectures on Wavelets
Published by Society for Industrial & Applied Mathematics (SIAM) ,1992
A theory for multiresolution signal decomposition: the wavelet representation
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1989
Gabor filters as texture discriminator
Biological Cybernetics, 1989