Paper Info
Title
Texture classification using relative phase and Gaussian mixture models in the complex wavelet domain
Abstract
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.
Year
DOI
Venue
2016
10.1109/AICCSA.2016.7945647
2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)
Keywords
Field
DocType
Gaussian Mixture Models,Relative phase,Generalized Gaussian model,Complex transforms,Texture
Feature vector,Pattern recognition,Image texture,Computer science,Precision and recall,Feature extraction,Artificial intelligence,Estimation theory,Contextual image classification,Mixture model,Multiclass classification
Conference
ISSN
ISBN
Citations 
2161-5322
978-1-5090-4321-7
0
PageRank 
References 
Authors
0.34
17
5
Name
Order
Citations
PageRank
Hind Oulhaj1121.62
Mohammed Rziza28918.32
Aouatif Amine3859.29
Rachid Jennane4265.47
Mohammed El Hassouni513529.52