FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images.

The application of large field-of-view (FoV) cameras equipped with fish-eye lenses brings notable advantages to various real-world computer vision applications, including autonomous driving. While deep learning has proven successful in conventional computer vision applications using regular perspective images, its potential in fish-eye camera contexts remains largely unexplored due to limited datasets for fully supervised learning. Semi-supervised learning comes as a potential solution to manage this challenge. In this study, we explore and benchmark two popular semi-supervised methods from the perspective image domain for fish-eye image segmentation. We further introduce FishSegSSL, a novel fish-eye image segmentation framework featuring three semi-supervised components: pseudo-label filtering, dynamic confidence thresholding, and robust strong augmentation. Evaluation on the WoodScape dataset, collected from vehicle-mounted fish-eye cameras, demonstrates that our proposed method enhances the model's performance by up to 10.49% over fully supervised methods using the same amount of labeled data. Our method also improves the existing image segmentation methods by 2.34%. To the best of our knowledge, this is the first work on semi-supervised semantic segmentation on fish-eye images. Additionally, we conduct a comprehensive ablation study and sensitivity analysis to showcase the efficacy of each proposed method in this research.

Perceptions of self-monitoring dietary intake according to a plate-based approach: A qualitative study.

Dietary self-monitoring is a behaviour change technique used to help elicit and sustain dietary changes over time. Current dietary self-monitoring tools focus primarily on itemizing foods and counting calories, which can be complex, time-intensive, and dependent on health literacy. Further, there are no dietary self-monitoring tools that conform to the plate-based approach of the 2019 Canada Food Guide (CFG), wherein the recommended proportions of three food groups are visually represented on a plate without specifying daily servings or portion sizes. This paper explored the perceptions of end-users (i.e., general public) and Registered Dietitians of iCANPlateTM-a dietary self-monitoring mobile application resembling the CFG. Qualitative data were collected through virtual focus groups. Focus group questions were based on the Capability, Opportunity, Motivation-Behaviour (COM-B) theoretical framework to explore perceptions of using the CFG and currently available dietary self-monitoring tools. The prototype iCANPlateTM (version 0.1) was presented to gain feedback on perceived barriers and facilitators of its use. Focus group discussions were audio recorded and verbatim transcribed. Trained researchers used thematic analysis to code and analyze the transcripts independently. Seven focus groups were conducted with Registered Dietitians (n = 44) and nine focus groups with members from the general public (n = 52). During the focus groups, participants mainly discussed the capabilities and opportunities required to use the current iteration of iCANPlateTM. Participants liked the simplicity of the application and its capacity to foster self-awareness of dietary behaviours rather than weight control or calorie counting. However, concerns were raised regarding iCANPlateTM's potential to improve adherence to dietary self-monitoring due to specific characteristics (i.e., insufficient classifications, difficulty in conceptualizing proportions, and lack of inclusivity). Overall, participants liked the simplicity of iCANPlateTM and its ability to promote self-awareness of dietary intakes, primarily through visual representation of foods on a plate as opposed to reliance on numerical values or serving sizes, were benefits of using the app. Findings from this study will be used to further develop the app with the goal of increasing adherence to plate-based dietary approaches.

Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency.

Smart meter datasets have recently transitioned from monthly intervals to one-second granularity, yielding invaluable insights for diverse metering functions. Clustering analysis, a fundamental data mining technique, is extensively applied to discern unique energy consumption patterns. However, the advent of high-resolution smart meter data brings forth formidable challenges, including non-Gaussian data distributions, unknown cluster counts, and varying feature importance within high-dimensional spaces. This article introduces an innovative learning framework integrating the expectation-maximization algorithm with the minimum message length criterion. This unified approach enables concurrent feature and model selection, finely tuned for the proposed bounded asymmetric generalized Gaussian mixture model with feature saliency. Our experiments aim to replicate an efficient smart meter data analysis scenario by incorporating three distinct feature extraction methods. We rigorously validate the clustering efficacy of our proposed algorithm against several state-of-the-art approaches, employing diverse performance metrics across synthetic and real smart meter datasets. The clusters that we identify effectively highlight variations in residential energy consumption, furnishing utility companies with actionable insights for targeted demand reduction efforts. Moreover, we demonstrate our method's robustness and real-world applicability by harnessing Concordia's High-Performance Computing infrastructure. This facilitates efficient energy pattern characterization, particularly within smart meter environments involving edge cloud computing. Finally, we emphasize that our proposed mixture model outperforms three other models in this paper's comparative study. We achieve superior performance compared to the non-bounded variant of the proposed mixture model by an average percentage improvement of 7.828%.

Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration.

In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is determined based on the minimum message length criterion (MML). The KL divergence between these two mixture models is utilized as the loss function for stochastic optimization to find the optimal parameters of the transformation model. The self-built point clouds are used to evaluate the performance of the proposed algorithm on rigid registration. Experiments demonstrate that the algorithm dramatically reduces the impact of noise and outliers and effectively extracts the key features of the data-intensive regions.

Human Activity Recognition with an HMM-Based Generative Model.

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded-scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications.

Cross-collection topic models extend previous single-collection topic models, such as Latent Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic modeling is to model document-topic representations and reveal similarities between each topic and differences among groups. However, the restriction of Dirichlet prior and the significant privacy risk have hampered those models' performance and utility. Training those cross-collection topic models may, in particular, leak sensitive information from the training dataset. To address the two issues mentioned above, we propose a novel model, cross-collection latent Beta-Liouville allocation (ccLBLA), which operates a more powerful prior, Beta-Liouville distribution with a more general covariance structure that enhances topic correlation analysis. To provide privacy protection for the ccLBLA model, we leverage the inherent differential privacy guarantee of the Collapsed Gibbs Sampling (CGS) inference scheme and then propose a hybrid privacy protection algorithm for the ccLBLA model (HPP-ccLBLA) that prevents inferring data from intermediate statistics during the CGS training process without sacrificing its utility. More crucially, our technique is the first attempt to use the cross-collection topic model in image classification applications and investigate the cross-collection topic model's capabilities beyond text analysis. The experimental results for comparative text mining and image classification will show the merits of our proposed approach.

Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling.

Probabilistic topic models are considered as an effective framework for text analysis that uncovers the main topics in an unlabeled set of documents. However, the inferred topics by traditional topic models are often unclear and not easy to interpret because they do not account for semantic structures in language. Recently, a number of topic modeling approaches tend to leverage domain knowledge to enhance the quality of the learned topics, but they still assume a multinomial or Gaussian document likelihood in the Euclidean space, which often results in information loss and poor performance. In this article, we propose a Bayesian embedded spherical topic model (ESTM) that combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.

Bayesian Matrix Factorization for Semibounded Data.

Bayesian non-negative matrix factorization (BNMF) has been widely used in different applications. In this article, we propose a novel BNMF technique dedicated to semibounded data where each entry of the observed matrix is supposed to follow an Inverted Beta distribution. The model has two parameter matrices with the same size as the observation matrix which we factorize into a product of excitation and basis matrices. Entries of the corresponding basis and excitation matrices follow a Gamma prior. To estimate the parameters of the model, variational Bayesian inference is used. A lower bound approximation for the objective function is used to find an analytically tractable solution for the model. An online extension of the algorithm is also proposed for more scalability and to adapt to streaming data. The model is evaluated on five different applications: part-based decomposition, collaborative filtering, market basket analysis, transactions prediction and items classification, topic mining, and graph embedding on biomedical networks.

Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings.

In recent years, clustering methods based on deep generative models have received great attention in various unsupervised applications, due to their capabilities for learning promising latent embeddings from original data. This article proposes a novel clustering method based on variational autoencoder (VAE) with spherical latent embeddings. The merits of our clustering method can be summarized as follows. First, instead of considering the Gaussian mixture model (GMM) as the prior over latent space as in a variety of existing VAE-based deep clustering methods, the von Mises-Fisher mixture model prior is deployed in our method, leading to spherical latent embeddings that can explicitly control the balance between the capacity of decoder and the utilization of latent embedding in a principled way. Second, a dual VAE structure is leveraged to impose the reconstruction constraint for the latent embedding and its corresponding noise counterpart, which embeds the input data into a hyperspherical latent space for clustering. Third, an augmented loss function is proposed to enhance the robustness of our model, which results in a self-supervised manner through the mutual guidance between the original data and the augmented ones. The effectiveness of the proposed deep generative clustering method is validated through comparisons with state-of-the-art deep clustering methods on benchmark datasets. The source code of the proposed model is available at https://github.com/fwt-team/DSVAE.

Grouped Spherical Data Modeling Through Hierarchical Nonparametric Bayesian Models and Its Application to fMRI Data Analysis.

Recently, spherical data (i.e., L2 normalized vectors) modeling has become a promising research topic in various real-world applications (such as gene expression data analysis, document categorization, and gesture recognition). In this work, we propose a hierarchical nonparametric Bayesian model based on von Mises-Fisher (VMF) distributions for modeling spherical data that involve multiple groups, where each observation within a group is sampled from a VMF mixture model with an infinite number of components allowing them to be shared across groups. Our model is formulated by employing a hierarchical nonparametric Bayesian framework known as the hierarchical Pitman-Yor (HPY) process mixture model, which possesses a power-law nature over the distribution of the components and is particularly useful for data distributions with heavy tails and skewness. To learn the proposed HPY process mixture model with VMF distributions, we systematically develop a closed-form optimization algorithm based on variational Bayes (VB). The merits of the proposed hierarchical Bayesian nonparametric model for modeling grouped spherical data are demonstrated through experiments on both synthetic data and a real-world application about resting-state functional magnetic resonance imaging (fMRI) data analysis.

Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning.

Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along with various features, were fed into several algorithms to predict future occupancy. This paper mainly proposes a weakly supervised occupancy prediction framework based on office sensor readings and occupancy estimations derived from an interactive learning approach. Two studies are the main emphasis of this paper. The first is the prediction of three occupancy states, referred to as discrete states: absence, presence of one occupant, and presence of more than one occupant. The purpose of the second study is to anticipate the future number of occupants, i.e., continuous states. Extensive simulations were run to demonstrate the merits of the proposed prediction framework's performance and to validate the interactive learning-based approach's ability to contribute to the achievement of effective occupancy prediction. The results reveal that LightGBM, a machine learning model, is a better fit for short-term predictions than known recursive neural networks when dealing with a limited dataset. For a 24 h window forecast, LightGBM improved accuracy from 38% to 50%, which is an excellent result for non-aggregated data (single office).

Sparse Count Data Clustering Using an Exponential Approximation to Generalized Dirichlet Multinomial Distributions.

Clustering frequency vectors is a challenging task on large data sets considering its high dimensionality and sparsity nature. Generalized Dirichlet multinomial (GDM) distribution is a competitive generative model for count data in terms of accuracy, yet its parameters estimation process is slow. The exponential-family approximation of the multivariate Polya distribution has shown to be efficient to train and cluster data directly, without dimensionality reduction. In this article, we derive an exponential-family approximation to the GDM distributions, and we call it (EGDM). A mixture model is developed based on the new member of the exponential-family of distributions, and its parameters are learned through the deterministic annealing expectation-maximization (DAEM) approach as a new clustering algorithm for count data. Moreover, we propose to estimate the optimal number of EGDM mixture components based on the minimum message length (MML) criterion. We have conducted a set of empirical experiments, concerning text, image, and video clustering, to evaluate the proposed approach performance. Results show that the new model attains a superior performance, and it is considerably faster than the corresponding method for GDM distributions.

Clustering Analysis via Deep Generative Models With Mixture Models.

Clustering is a fundamental problem that frequently arises in many fields, such as pattern recognition, data mining, and machine learning. Although various clustering algorithms have been developed in the past, traditional clustering algorithms with shallow structures cannot excavate the interdependence of complex data features in latent space. Recently, deep generative models, such as autoencoder (AE), variational AE (VAE), and generative adversarial network (GAN), have achieved remarkable success in many unsupervised applications thanks to their capabilities for learning promising latent representations from original data. In this work, first we propose a novel clustering approach based on both Wasserstein GAN with gradient penalty (WGAN-GP) and VAE with a Gaussian mixture prior. By combining the WGAN-GP with VAE, the generator of WGAN-GP is formulated by drawing samples from the probabilistic decoder of VAE. Moreover, to provide more robust clustering and generation performance when outliers are encountered in data, a variant of the proposed deep generative model is developed based on a Student's-t mixture prior. The effectiveness of our deep generative models is validated though experiments on both clustering analysis and samples generation. Through the comparison with other state-of-art clustering approaches based on deep generative models, the proposed approach can provide more stable training of the model, improve the accuracy of clustering, and generate realistic samples.

Maximum A Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection.

One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.

Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions.

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis.

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

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.

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition.

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation-maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures.

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.

Modeling and Clustering Positive Vectors via Nonparametric Mixture Models of Liouville Distributions.

In this article, we propose an effective mixture model-based approach to modeling and clustering positive data vectors. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution which is extracted from the family of Liouville distributions. To cope with the problem of determining the appropriate number of clusters in our approach, a nonparametric Bayesian framework is used to extend the IBL mixture to an infinite mixture model in which the number of clusters is assumed to be infinite initially and will be inferred automatically during the learning process. To optimize the proposed model, we propose a convergence-guaranteed learning algorithm based on the averaged collapsed variational Bayes inference that can effectively learn model parameters with closed-form solutions. The effectiveness of the proposed infinite IBL mixture model for modeling and clustering positive vectors is validated through both synthetic and real-world data sets.

Variational Bayesian Learning of Generalized Dirichlet-Based Hidden Markov Models Applied to Unusual Events Detection.

Learning a hidden Markov model (HMM) is typically based on the computation of a likelihood which is intractable due to a summation over all possible combinations of states and mixture components. This estimation is often tackled by a maximization strategy, which is known as the Baum-Welch algorithm. However, some drawbacks of this approach have led to the consideration of Bayesian methods that add a prior over the parameters in order to work with the posterior probability and the marginal likelihood. These approaches can lead to good models but to the cost of extremely long computations (e.g., Markov Chain Monte Carlo). More recently, variational Bayesian frameworks have been proposed as a Bayesian alternative that keeps the computation tractable and the approximation tight. It relies on the introduction of a prior over the parameters to be learned and on an approximation of the true posterior distribution. After proving good standing in the case of finite mixture models and discrete and Gaussian HMMs, we propose here to derive the equations of the variational learning of the Dirichlet mixture-based HMM, and to extend it to the generalized Dirichlet. The latter case presents several properties that make the estimation more accurate. We prove the validity of this approach within the context of unusual event detection in public areas using the University of California San Diego data sets. HMMs are trained over normal video sequences using the typical Baum-Welch approach versus the variational one. The variational learning leads to more accurate models for the detection and localization of anomaly, and the general HMM approach is shown to be versatile enough to handle the detection of various synthetically generated tampering events.