Texture Modeling Using Contourlets and Finite Mixtures of Generalized Gaussian Distributions and Applications

In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions ( MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT,...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2014-04, Vol.16 (3), p.772-784
Main Authors: Allili, Mohand Said, Baaziz, Nadia, Mejri, Marouene
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computed tomography
Computer science
control theory
systems
Exact sciences and technology
Fabric texture defect detection
Fabrics
Gaussian distribution
Hidden Markov models
IR face recognition
Mathematical analysis
Mathematical models
Mixture of Generalized Gaussian distributions (MoGG)
Normal distribution
Pattern recognition. Digital image processing. Computational geometry
Probability density functions
Redundant
Shape
standard/redundant contourlet transforms
Surface layer
Texture
texture retrieval
Wavelet transforms
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Summary:In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions ( MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT ( SCT) and redundant CT ( RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2014.2298832