Bayesian learning of finite generalized Gaussian mixture models on images

Elguebaly, Tarek; Bouguila, Nizar

doi:10.1016/j.sigpro.2010.08.014

Cited by 71 publications

(29 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7 Alternatively, we can also set this threshold to a fixed value, ε t = ε, as done in the simulations. In this case, setting ε ≥ M π implies that the update of S t never happens (i.e., new support points are never added to the support set), whereas candidate nodes would be incorporated to S t almost surely by setting ε → 0.…”

Section: Examples Of Update Rulesmentioning

confidence: 99%

“…Markov chain Monte Carlo (MCMC) methods [1,2] are very important tools for Bayesian inference and numerical approximation, which are widely employed in signal processing [3][4][5][6][7] and other related fields [1,8]. A crucial issue in MCMC is the choice of a proposal probability density function (pdf ), as this can strongly affect the mixing of the MCMC chain when the target pdf has a complex structure, e.g., multimodality and/or heavy tails.…”

Section: Introductionmentioning

confidence: 99%

“…The underlying theoretical support is based on the approach introduced in [14]. The new schemes are particularly suitable for sampling from complicated full-conditional pdfs within a Gibbs sampler [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive independent sticky MCMC algorithms

Martino

Casarin

Leisen

et al. 2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Monte Carlo methods have become essential tools to solve complex Bayesian inference problems in different fields, such as computational statistics, machine learning, and statistical signal processing. In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky Markov Chain Monte Carlo (MCMC) algorithms, to sample efficiently from any bounded target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities, which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively from previously drawn samples. The algorithm's efficiency is ensured by a test that supervises the evolution of the set of support points. This extra stage controls the computational cost and the convergence of the proposal density to the target. Each part of the novel family of algorithms is discussed and several examples of specific methods are provided. Although the novel algorithms are presented for univariate target densities, we show how they can be easily extended to the multivariate context by embedding them within a Gibbs-type sampler or the hit and run algorithm. The ergodicity is ensured and discussed. An overview of the related works in the literature is also provided, emphasizing that several well-known existing methods (like the adaptive rejection Metropolis sampling (ARMS) scheme) are encompassed by the new class of algorithms proposed here. Eight numerical examples (including the inference of the hyper-parameters of Gaussian processes, widely used in machine learning for signal processing applications) illustrate the efficiency of sticky schemes, both as stand-alone methods to sample from complicated one-dimensional pdfs and within Gibbs samplers in order to draw from multi-dimensional target distributions.

show abstract

Section: Examples Of Update Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive independent sticky MCMC algorithms

Martino

Casarin

Leisen

et al. 2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…When the Gaussian distribution is naturally generalized, it becomes a GGD. The pdf (probability density function) of GGD is given by (Elguebaly and Bouguila, 2011) …”

Section: Statistical Modeling Of DImentioning

confidence: 99%

Tsunami-induced Change Detection Using SAR Intensity and Texture Information Based on the Generalized Gaussian Mixture Model

Jung¹,

Kim²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

View full text Add to dashboard Cite

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

show abstract

“…À μ l X À Á À denotes generalized distribution process. Density function p(x i |μ i ) [29,30] represents the mixing process. Figure 4 found that, the maximum coefficient value generalized distribution density function is always increasing with the increase of μ, which is set to different values that indicate that it is necessary to select the optimum weight based on the actual requirement of image processing.…”

Section: Image Transmission Based On Opportunistic Networkingmentioning

confidence: 99%

Image processing and transmission scheme based on generalized Gaussian mixture with opportunistic networking for wireless sensor networks

Hao

2015

J Wireless Com Network

View full text Add to dashboard Cite

For satisfying the quality of service (QoS) requirements and image denoising services in wireless sensor network (WSN) applications, based on opportunistic networking technology and generalized Gaussian mixture algorithm, an adaptive image processing and transmission scheme is proposed in this paper. According to the real-time state record matrix, the multi-objective optimization scheme with equalizer coefficients and the opportunistic cooperative scheme in view of energy and computing ability are studied, respectively. Then, the generalized Gaussian mixture algorithm is used to reduce the image data and eliminate the noise interference from the WSN environment. Finally, Simulation results show that the proposed scheme has better QoS support capability results such as reliability, real-time performance, and energy efficiency, as well as the image decoding accuracy including peak signal to noise ratio.

show abstract

Bayesian learning of finite generalized Gaussian mixture models on images

Cited by 71 publications

References 45 publications

Adaptive independent sticky MCMC algorithms

Adaptive independent sticky MCMC algorithms

Tsunami-induced Change Detection Using SAR Intensity and Texture Information Based on the Generalized Gaussian Mixture Model

Image processing and transmission scheme based on generalized Gaussian mixture with opportunistic networking for wireless sensor networks

Contact Info

Product

Resources

About