Quaternionic wavelets for texture classification

This paper proposes a new texture classifier based on the Quaternionic Wavelet Transform (QWT). This recent transform separates the informations contained in the image better than a classical wavelet transform (DWT), and provides a multiscale image analysis which coefficients are 2D analytic, with one near-shift invariant magnitude and a phase, that is made of three angles. The interpretation and use of the QWT coefficients, especially the phase, are discussed, and we present a texture classifier using both the QWT magnitude and the QWT phase of images. Our classifier performs a better recognition rate than a standard wavelet based classifier.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Raphaël Soulard, Philippe Carré
• Keywords: ICASSP 2010, Quaternionic Wavelet Transform, Signal Processing, Texture Classifier, Wavelet