Wavelets For Multiscale Texture Analysis

The discrete wavelet transform maps an image on a series of wavelet detail coefcients, which constitute a multiscale representation of the image. We conjecture that texture can be characterized by the statistics of these coe cients and introduce 2 feature sets for this purpose: the wavelet histogram signatures and the wavelet cooccurrence signatures. Using a model based approach, we show that all rst order statistics of the wavelet detail coe cients can be captured in the wavelet histogram signatures. Further improvement in texture characterization can be obtained from the coefcients second order statistics, which are re ected in the wavelet cooccurrence signatures. We verify experimentally the validity of the model for the detail histogram and compare the introduced feature sets to the traditionally used energy signatures in a classi cation experiment. Best classi cation performance is shown to be achieved by combining histogram and cooccurrence signatures.

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