On Gauss Mixture Vector Quantizers and Gabor Wavelet Classifiers for Texture Classification

For texture classification, the observation probability distribution for each texture can be estimated using a Gauss mixture vector quantizer (GMVQ) designed with the generalized Lloyd algorithm with a minimum discrimination information (MDI) distortion. The designed multiple GMVQs are applied to classifying Brodatz textures. For low complexity implementation, Super-blocks are used to capture the macro features of the texture. In [1], the results were compared well to TSWT [2]. As an extension of [1], this paper shows that our multi-codebook GMVQ classifier, applied to the Brodatz texture database, outperforms state-ofthe-art texture classifier, Gabor wavelet classifier [3].