Texture classification using relative phase and Gaussian mixture models in the complex wavelet domain
Hind Oulhaj, Mohammed Rziza, Aouatif Amine, Rachid Jennane, Mohammed El Hassouni · 2016
The importance of phase features for texture analysis has been earlier established for many image processing applications. However, the modeling of the phase data faces some difficulties as its information gathers data with rotating values and thus highly sensitive to distortions. Motivated by its ability to capture different shapes of histograms, in this communication, we propose the Gaussian Mixture Model (GMM) to characterize the behavior of relative phase. The Maximum-likelihood Estimator (MLE) is used to estimate the GMM parameters. To investigate the relevance of the GMM model for relative phase data, a feature vector incorporating the estimated parameters is proposed for a multiclass classification task. Experiments are conducted on textures from VisTex and Brodatz databases. Results demonstrate that the GMM model fit well relative phase data. In addition, higher rates of accuracy, precision and recall, 95.35%, 95.50% and 95.40%, respectively, were achieved for Brodatz textures using the proposed feature vector. This suggests the potential usefulness of the probabilistic proprieties for texture analysis.