Spatially Constrained Mixture Model with Feature Selection for Image and Video Segmentation

Channoufi, Ines; Bourouis, Sami; Bouguila, Nizar; Hamrouni, Kamel

doi:10.1007/978-3-319-94211-7_5

Cited by 7 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is important to note that some poor results are obtained (for all involved methods in this work) and this can probably be explained by the difficulty of identifying the exact texture class due to its different rotations and changes in viewpoints. One possible solution, which we are starting to work on, is to introduce a feature selection mechanism, as in [67,68], to improve the classification performance. Another challenge we face with our method is to fine-tune hyperparameters that may have an impact on classification and recognition accuracy.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, some developed mixtures were applied successfully in the case of forgery detection problem [21,30]. In this context, we address the problem of image forgery detection by investigating recent developed mixture model named finite bounded generalized Gaussian mixtures (BGGMM) [31,32]. The consideration of BGGMM is encouraged by the fascinating results exposed recently and show this model as more effective for data classification and modeling than the conventional Gaussian mixtures [31,26].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…On the other hand, data-features can be either informative (relevant) or uninformative (irrelevant). Considering all possible features will augment the computational cost and becomes an obstacle against high performance as cited in [30]- [34]. In fact, the presence of irrelevant features can form new false clusters and this issue may lead to raise the false positive intrusion detection rate and make the overall process time consuming.…”

Section: Motivationsmentioning

confidence: 99%

Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection

et al. 2019

Self Cite

View full text Add to dashboard Cite

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network activity and to protect systems and the Internet of Things (IoT) devices from attacks (or intrusions). The problem with these systems is that they generate a huge amount of inappropriate false alarms whenever abnormal activities are detected and they are not too flexible for a complex environment. The high-level rate of the generated false alarms reduces the performance of IDS against cyber-attacks and makes the tasks of the security analyst particularly difficult and the management of intrusion detection process computationally expensive. We study here one of the challenging aspects of computer and network security and we propose to build a detection model for both known and unknown intrusions (or anomaly detection) via a novel nonparametric Bayesian model. The design of our framework can be extended easily to be adequate for IoT technology and notably for intelligent smart city web-based applications. In our method, we learn the patterns of the activities (both normal and anomalous) through a Bayesian-based MCMC inference for infinite bounded generalized Gaussian mixture models. Contrary to classic clustering methods, our approach does not need to specify the number of clusters, takes into consideration the uncertainty via the introduction of prior knowledge for the parameters of the model, and permits to solve problems related to over-and under-fitting. In order to get better clustering performance, feature weights, model's parameters, and the number of clusters are estimated simultaneously and automatically. The developed approach was evaluated using popular data sets. The obtained results demonstrate the efficiency of our approach in detecting various attacks. INDEX TERMS Intrusion detection systems (IDS), anomaly intrusion detection, infinite mixture models, bounded generalized Gaussian models, Bayesian inference, Markov chain Monte Carlo (MCMC).

show abstract

Spatially Constrained Mixture Model with Feature Selection for Image and Video Segmentation

Cited by 7 publications

References 24 publications

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

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

Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection

Contact Info

Product

Resources

About