A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

Rami, Hassan; Maliani, Ahmed Drissi El

doi:10.1109/access.2019.2939466

Cited by 2 publications

(4 citation statements)

References 47 publications

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“…In our future work we will study if the proposed framework can be extended via nonparametric Bayesian principle in order to increase the classification accuracy. Future works could be also devoted to avoiding the limitations of Monte-Carlo approximations by considering for example the Cauchy-Schwarz divergence as previously done successfully for different mixture models [23], [29], [30]. We plan to investigate this work for other tasks such as image segmentation by classifying the smaller regions and evaluate it for other related image classification tasks such as object recognition.…”

Section: Discussionmentioning

confidence: 99%

“…Some texture-based descriptors are developed and are invariant to many geometric transformations. It is possible to extract texture features from images with different techniques, including statistical methods that often rely on higher order statistics which allow different measurements to be accurately calculated [29]- [31]. In this study, we are primarily motivated by local image information that describes image in more details.…”

Section: Lbp-based Features Extractionmentioning

confidence: 99%

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A Competitive Generalized Gamma Mixture Model for Medical Image Diagnosis

2021

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Parametric family of statistical distributions are of great importance for several applications.In particular, we propose to investigate the generalized Gamma mixture model (g MM) for modeling and classifying medical imaging (Chest x-ray and CT-scans). The main advantage of this mixture over some existing Gaussian models is that it allows additional flexibility in shape modeling, which is crucial for classification systems. In order to capture accurately the intrinsic nature of medical images, we propose to derive some efficient measures based on Fisher, Kullback-Leibler and Bhattacharyya distances for the mixtures of generalized Gamma distributions. Indeed, the main idea is to investigate these distances effectively via the statistical model parameters in order to make our proposed scheme particularly appropriate for image classification problem. The proposed approach involves the extraction of robust texture descriptors, the learning of mixture model g MM via the expectation-maximization (EM) and Newton-Raphson algorithms, and the classification of images using the derived mixtures-based distances. We evaluate our model against the challenging problem of early diagnosis of pneumonia diseases. Experimental results on different datasets show the merits of our developed framework compared with the other methods.

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Section: Discussionmentioning

confidence: 99%

Section: Lbp-based Features Extractionmentioning

confidence: 99%

A Competitive Generalized Gamma Mixture Model for Medical Image Diagnosis

2021

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“…In order to construct GMM, the following unknown parameters need to be determined:

Θ = ({},,, \{ω^{1}, μ^{1}, \sum^{1}\}, \dots, \{ω^{N}, μ^{N}, \sum^{N}\})

Some commonly used parameter learning methods are maximum likelihood estimation (MLE), expectation maximization (EM), and Figueiredo‐Jain (F‐J). [ 28–30 ] Through EM algorithm iteration, Equations () are obtained:

μ_{k}^{(s + 1)} = \frac{\sum_{j = 1}^{n} p^{(s)} (()|, C_{k}, x_{j}) x_{j}}{\sum_{j = 1}^{n} p^{(s)} (()|, C_{k}, x_{j})}

\sum_{k}^{(s + 1)} = \frac{\sum_{j = 1}^{n} p^{(s)} (()|, C_{k}, x_{j}) ((), x_{j} - μ_{k}^{(s + 1)}) {(x_{j} - μ_{k}^{(s + 1) T})}^{T}}{\sum_{j = 1}^{n} p^{(s)} (()|, C_{k}, x_{j})}

ω_{k}^{(s + 1)} = \frac{\sum_{j = 1}^{n} p^{(s)} (()|, C_{k}, x_{j})}{n}

where μ k ( s + 1) , ∑ k ( s + 1) , and ω k ( s + 1) represent the mean, covariance matrix, and weight in the ( s + 1) th iteration, respectively.…”

Section: Operation Status Assessment For a Fused Magnesium Furnacementioning

confidence: 99%

“…Some commonly used parameter learning methods are maximum likelihood estimation (MLE), expectation maximization (EM), and Figueiredo-Jain (F-J). [28][29][30] Through EM algorithm iteration, Equations ( 19)-( 21) are obtained:…”

Section: Operation Status Assessment Based On Gmmmentioning

confidence: 99%

Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

2021

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Based on operation status assessment and self‐optimizing regulation, an optimization strategy for the smelting process of a fused magnesium furnace is proposed to improve the benefits of smelting magnesia. In the online evaluation stage, according to the qualitative information, the data is divided by the fuzzy model recognition method, and then the posterior probability of online data in different Gaussian mixture models is calculated to obtain the evaluation results. When the evaluation results are not optimal, the non‐optimal variables are obtained by calculating the contribution rate, and then the self‐optimizing adjustment is carried out by case search. In order to improve the recovery rate of self‐optimizing regulation, a time series prediction method is used to predict the operation status, and the corresponding operation schemes for different prediction results are proposed. Experimental results show that the proposed method is accurate and effective.

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A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

Cited by 2 publications

References 47 publications

A Competitive Generalized Gamma Mixture Model for Medical Image Diagnosis

A Competitive Generalized Gamma Mixture Model for Medical Image Diagnosis

Self‐optimization for smelting process of fused magnesium furnace based on operation status assessment

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