A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution

Said Benlakhdar1, Mohammed Rziza1, Rachid Oulad Haj Thami2

1LRIT URAC 29, Faculty of Sciences, Mohammed V University in Rabat, Morocco.

2RIITM, ENSIAS, Mohammed V University in Rabat, Morocco.




Image classification refers to an important process in computer vision. The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in wavelet domain to describe textured images and rely on number of principles which give its internal coherence and originality. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform. Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM). The results, obtained for two benchmark datasets, show that the proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models see (Kohavi, 1995). Moreover, it shows other advantages relied to Noise-resistant and rotation invariant.

Cite as:

Benlakhdar, S., Rziza, M., & Thami, R.O.H. (2020). A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution. Computer Methods in Materials Science, 20(3), 95–106. https://doi.org/10.7494/cmms.2020.3.0724

Article (PDF):


Statistical image modeling, SIFT, Mixture gamma distribution, Uniform discrete curvelet transform, Classification


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