Quaternionic Wavelets for Texture Classification

Raphaël Soulard (1) , Philippe Carré (1)

1 SIC

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.

Mots clés: Wavelet transforms; 2D Phase; Quaternionic Wavelet Transform; Image texture analysis; Image classification

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Raphaël Soulard, Philippe Carré. Quaternionic Wavelets for Texture Classification. 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), Mar 2010, Dallas, TX, United States. pp.4134--4137. ⟨hal-00475395⟩