Unsupervised Human Action Categorization Using a Riemannian Averaged Fixed-Point Learning of Multivariate GGMM

Najar, Fatma; Bourouis, Sami; Zaguia, Atef; Bouguila, Nizar; Belghith, Safya

doi:10.1007/978-3-319-93000-8_46

Cited by 17 publications

(8 citation statements)

References 20 publications

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“…In particular, classifying non-Gaussian data in an unsupervised way can be of great interest for automated medical applications. Among the main existing methods to tackle this problem, statistical mixture models have recently gained considerable interest from both the theoretical and practical points of view [ 15 , 16 , 17 , 18 , 19 , 20 ]. This approach has led to the design of new more efficient tools.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021

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Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework.

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Section: Introduction and Related Workmentioning

confidence: 99%

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021

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“…Recently, multimedia categorization and recognition are becoming two challenging research problems that have attracted a lot of attention for several applications [49][50][51][52]. Object categorization refers to the task of labelling objects into one of the predefined and meaningful categories and this step is mainly based on extracting effective visual features.…”

Section: Human Actions Categorizationmentioning

confidence: 99%

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

et al. 2021

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This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components. Robust descriptors are derived from encoding features with the Fisher vector method, which considers higher order statistics. These descriptors are combined with a linear support vector machine classifier in order to achieve higher accuracy. The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification and human activity categorization.

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“…Statistical approaches provide a formal way for image modelling and classification [25,26]. In particular, finite mixture models have attracted great interest among other approaches [25,27,28] [29]. Recently, some developed mixtures were applied successfully in the case of forgery detection problem [21,30].…”

Section: Inpainting Forgery Detectionmentioning

confidence: 99%

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

Alharbi

Alhakami

Bourouis

et al. 2020

ACI

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We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

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Unsupervised Human Action Categorization Using a Riemannian Averaged Fixed-Point Learning of Multivariate GGMM

Cited by 17 publications

References 20 publications

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

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