Gaussian Mixture Model Framework Research Articles

Developing a Hidden Markov Model - Gaussian Mixture Model Framework to Classify Gait Patterns in Huntington's Disease

Developing a Hidden Markov Model - Gaussian Mixture Model Framework to Classify Gait Patterns in Huntington's Disease

3D Holoscopic Image Compression Based on Gaussian Mixture Model

We introduce a Gaussian Mixture Model (GMM) framework for 3D holoscopic image compression in this paper. The elemental-images of the 3D holoscopic image are predicted using GMM and the parameters of GMM are estimated using the common Expectation-Maximization (EM) algorithm. GMM Model Optimization (GMO) is used in this framework to select the optimal number of distributions and avoid local optimum of EM at the same time. A three-dimensional distribution-rotation based decomposition is proposed to change covariance parameters to meaningful features and improve the coding efficiency. The features and the remaining parameters of the GMM are encoded using fixed-length bits. A feature-based dictionary is proposed in this framework to match the similar Gaussian distributions utilizing the similar GMM features. And the offsets of the matched distributions are recorded as motion vectors to replace the similar areas in the elemental-images of the 3D holoscopic image. The residual between the original image and the prediction is encoded using Screen Content Coding Extension of High Efficiency Video Coding (HEVC-SCC). Experimental results show that our method performs better than HEVC-SCC, two coding methods based on pseudo-sequences and a state-of-the-art content-based compression method with Gaussian process regression.

Robust Group Anomaly Detection for Quasi-Periodic Network Time Series

Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.

A relaxed Gaussian mixture model framework for terrain classification based on distinct range datasets

ABSTRACT The classification of the range (3D) dataset in a meaningful way for the safe navigation of a mobile robot in uneven terrain is still a critical problem. In this paper, we address the problem of classifying a given 3D dataset into a set of known semantic classes, i.e., objects or ground. The classification of range data is carried out using normalized histogram features of 3D information by employing a variational framework of relaxed Gaussian mixture model (GMM). The main merits of the proposed method are independent of prior knowledge about the terrain, range data format and resolution while preserving objects and surface details. The experimental results show that the robustness of the proposed method by achieving classification accuracy of nearly 99.98% on distinct datasets.

CGMVAE: Coupling GMM Prior and GMM Estimator for Unsupervised Clustering and Disentanglement

Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method on top of one recent architecture with a novel explanation of Gaussian mixture model (GMM) membership, accompanied by a GMM loss to enhance the clustering. The GMM loss is optimized with the explicitly computed parameters under our coupled GMM inspired framework. Specifically, our model takes the advantage of implicitly learning a GMM in latent space by neural networks (GMM prior as the first GMM), and explicitly clustering via the other GMM framework (GMM estimator as the second GMM). We further introduce a Dirichlet conjugate loss as a regularization term to prevent the GMM estimator from degenerating to few Gaussians. Eventually, we further propose an application of apparel generation based on the proposed method which requires only three selection steps. Extensive experiments on publicly available datasets demonstrate the effectiveness of our method, in terms of clustering and disentanglement performance.

Adaptive Multi-Task Human-Robot Interaction Based on Human Behavioral Intention

Learning from demonstrations with Probabilistic Movement Primitives (ProMPs) has been widely used in robot skill learning, especially in human-robot collaboration. Although ProMP has been extended to multi-task situations inspired by the Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of the collaborative tasks in order to align with the instantaneous change of human intention. To solve this problem, we proposed an alternate learning-based parameter estimation method and an empirical minimum variation-based decomposition strategy with projection points, combining with linear interpolation strategy for weights, based on a Gaussian mixture model framework. Alternate learning of weights and parameters in multi-task ProMP (MTProMP) allows the robot to obtain a smooth composite trajectory planning which crosses expected via points. Decomposition strategy reflects how the desired via point state is projected onto the individual ProMP component, rendering the minimum total sum of deviations between each projection point with the respective prior. Linear interpolation is used to adjust the weights among sequential via points automatically. The proposed method and strategy are successfully extended to multi-task interaction ProMPs (MTiProMP). With MTProMP and MTiProMP, the robot can be applied to multiple tasks in industrial factories and collaborate with the worker to switch from one task to another according to changing intentions of the human. Classical via points trajectory planning experiments and human-robot collaboration experiments are performed on the Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better.

Trajectory Generation by Chance-Constrained Nonlinear MPC With Probabilistic Prediction.

Continued great efforts have been dedicated toward high-quality trajectory generation based on optimization methods; however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and incorporate the presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained and, thus, a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently, a nonlinear model predictive control problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and, thus, based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.

Automatic targetless camera–LIDAR calibration by aligning edge with Gaussian mixture model

AbstractThis paper presents a calibration algorithm that does not require an artificial target object to precisely estimate a rigid‐body transformation between a camera and a light detection and ranging (LIDAR) sensor. The proposed algorithm estimates calibration parameters by minimizing a cost function that evaluates the edge alignment between two sensor measurements. In particular, the proposed cost function is constructed using a projection model‐based many‐to‐many correspondence of the edges to fully exploit measurements with different densities (dense photometry and sparse geometry). The alignment of the many‐to‐many correspondence is represented using the Gaussian mixture model (GMM) framework. Here, each component of the GMM, including weight, displacement, and standard deviation, is derived to suitably capture the intensity, location, and influential range of the edge measurements, respectively. The derived cost function is optimized by the gradient descent method with an analytical derivative. A coarse‐to‐fine scheme is also applied by gradually decreasing the standard deviation of the GMM to enhance the robustness of the algorithm. Extensive indoor and outdoor experiments validate the claim that the proposed GMM strategy improves the performance of the proposed algorithm. The experimental results also show that the proposed algorithm outperforms previous methods in terms of precision and accuracy by providing calibration parameters of standard deviations less than 0.6° and 2.1 cm with a reprojection error of 1.78 for a 2.1‐megapixel image (2,048 × 1,024) in the best case.

Vocal Tract Length Normalization using a Gaussian mixture model framework for query-by-example spoken term detection

A speech spectrum is known to be changed by the variations in the length of the vocal tract of a speaker. This is because of the fact that speech formants are inversely related to the vocal tract length (VTL). The process of compensating spectral variation due to the length of the vocal tract is known as Vocal Tract Length Normalization (VTLN). VTLN is a very important speaker normalization technique for speech recognition and related tasks. In this paper, we used Gaussian Posteriorgram (GP) of VTL-warped spectral features for a Query-by-Example Spoken Term Detection (QbE-STD) task. This paper presents the use of a Gaussian Mixture Model (GMM) framework for VTLN warping factor estimation. In particular, the presented GMM framework does not require phoneme-level transcription. We observed the correlation between the VTLN warping factor estimates obtained via a supervised HMM-based approach and an unsupervised GMM-based approach. In addition, a phoneme recognition and speaker de-identification tasks were conducted using GMM-based VTLN warping factor estimates. For QbE-STD, we considered three spectral features, namely, Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), and MFCC-TMP (which uses Teager Energy Operator (TEO) to exploit implicitly magnitude and phase information in the MFCC framework). Linear frequency scaling variations for VTLN warping factor are incorporated into these three cepstral representations for the QbE-STD task. Similarly, VTL-warped Gaussian posteriorgram improved the Maximum Term Weighted Value by 0.021 (i.e., 2.1%), and 0.015 (i.e., 1.5%), for MFCC and PLP feature sets, respectively, on the evaluation set of the MediaEval SWS 2013 corpus. The better performance is primarily due to VTLN warping factor estimation using unsupervised GMM framework. Finally, the effectiveness of the proposed VTL-warped GP is presented to rescore using various detection sources, such as depth of detection valley, Self-Similarity Matrix, Pseudo Relevance Feedback and weighted mean features.

Adaptive spatio‐temporal background subtraction using improved Wronskian change detection scheme in Gaussian mixture model framework

Background subtraction (BS) is a fundamental step for moving object detection in various video surveillance applications. Gaussian mixture model (GMM) is a widely used BS technique which provides a good compromise between robustness to the background variations and real-time constraints. However, GMM does not support the spatial relationship among neighbouring pixels and it uses a fixed learning rate for every pixel during the parameter update. On the other hand, Wronskian change detection model (WM) is a spatial-domain BS technique which solves misclassification of pixels but fails in the presence of dynamic background. In this study, a novel spatio-temporal BS technique is proposed that exploits spatial relation of Wronskian function and employs it with a new fuzzy adaptive learning rate in a GMM framework. Instead of using WM directly, an improved WM is proposed by adaptively finding out the ratio of the current pixel to the background pixel or its reciprocal, and a weighted Wronskian is developed to mitigate the effect of dynamic background pixels. Additionally, a new fuzzy adaptive learning rate is employed in the GMM framework. Experimental results of the proposed framework yield better silhouette of the moving objects as compared with the state-of-the-art techniques.

Plug-and-Play Prior Based on Gaussian Mixture Model Learning for Image Restoration in Sensor Network

In this paper, we propose a method that use the Gaussian mixture model (GMM) as a plug-and-play prior for image restoration in sensor network. The “plug-and-play” concept as an image prior is extended to image restoration and just mentioned in recent years. Particularly, the plug-and-play alternating direction method of the multiplier is very fit for the GMM framework to regularize the sub-problems. In order to avoid error results caused by general minimum of mean square error criterion, we propose two spatial constraints: one applies the $K$ -nearest-neighbor method based on an Euclidean distance to measure the similarity of image patches in clustering step; the other adopts the Gaussian weight based on the Mahalanobis distance to update the mean vector and covariance matrix. Finally, we compare our method with several recent state-of-the-art methods, and the results show that our proposed method has good performance in preserving details and eliminating the staircase effect.

Enhanced speaker verification using an adaptive multiple low-rank representation based on the modified adaptive Gaussian mixture model framework

In this paper, a new method for the calculation of the observation-confidence value that is applied in the modified adaptive Gaussian mixture model framework is proposed for speaker verification. First, an adaptive version of the multiple low-rank representation method, for which a weighted decomposition that incorporates the prior information regarding the speech/non-speech content is considered, is proposed to find the enhanced speech and for the estimation of the frame signal-to-noise ratio (SNR) values. Then, a simple sigmoid function is applied to convert the frame SNR values into the observation-confidence values. To verify the accuracy of the system, we use utterances from the Korean movie You Came From The Stars. The experiment results show that our proposed approach achieves a greater accuracy compared with the other well-known baseline methods, such as the GMM-based universal background model, the GMM supervector-based support vector machine (SVM), the i-vector-based SVM, and the sparse representation, under the noisy environment.

Analysis of the Intrinsic Mode Functions for Speaker Information

This work explores the utility of the time-domain signal components, or the Intrinsic Mode Functions (IMFs), of speech signals’, as generated from the data-adaptive filterbank nature of Empirical Mode Decomposition (EMD), in characterizing speakers for the task of text-independent Speaker Verification (SV). A modified version of EMD, denoted as MEMD, which extracts IMFs with lesser mode-mixing, and provides a better representation of the higher frequency spectrum of speech, is also utilized for the SV task. Three different features are extracted over 20 ms frames, from the IMFs of EMD and MEMD. They are, then, tested individually, and in conjunction with the Mel Frequency Cepstral Coefficients (MFCCs), for SV. Two corpora - the NIST SRE 2003 corpus, and the CHAINS corpus - are used for the experiments. The results evaluated on the NIST SRE 2003 database, using the i-vector framework, reveal that the features extracted from the IMFs, in conjunction with the MFCCs, enhances the performance of the SV system. Further, it is observed that only a small set of lower-order IMFs is useful and necessary for characterizing speaker-specific information. The combination of the features with the MFCCs is also found to be useful when short speech utterances of ≤10 s are used for testing. Similarly, the results evaluated on the CHAINS corpus, using the conventional Gaussian Mixture Model (GMM) framework, reveal that the features, in combination with the MFCCs, enhance the performance of the SV system, not only for normal speech, but also for fast and whispered speech. Again, it is observed that only the first few IMFs are needed and useful for achieving such enhanced performance.

The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRI.

The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.

Analyzing features by SWLDA for the classification of HEp-2 cell images using GMM

In this paper, a system is introduced for automatic classification of Human Epithelial cells type 2 Patterns (HEp-2) in indirect immunofluorescence imaging. HEp-2 cell classification was performed using Step-Wise Linear Discriminant Analysis (SWLDA) and Gaussian Mixture Model (GMM). Images were first normalized. Then, binary, intensity, statistical, spectral, wavelet-based, Haralick, CLBP and Gabor features were extracted from the normalized images. The best features were then selected using SWLDA, and the GMM framework was used for classification. Two protocols were examined considering all data and divided data (into intermediate and positive groups). In the first protocol all data are modeled with one GMM and in the second protocol two GMM models are designed for intermediate and positive data. The methods were applied on the ICPR2012 and ICIP2013 datasets. For the ICPR2012 dataset, a third protocol was also proposed based on the results of the second protocol. The classification was evaluated using standard metrics. The comparative results show that our method outperformed previous works for the ICPR2012 dataset and intermediate for the ICIP2013 dataset.

Parameterized Distortion-Invariant Feature for Robust Tracking in Omnidirectional Vision

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to the involved quadratic mirrors. Therefore, features based on the conventional pin-hole model are hard to achieve satisfactory performances when directly applied to the distorted omnidirectional images. This paper analyzes the catadioptric geometry to facilitate modeling the nonlinear distortions of omnidirectional images. Different to the conventional imaging model, the prior information is considered in catadioptric system. A parameterized neighborhood mapping model is proposed to efficiently calculate the neighborhood of an object based on its measurable radial distance in the image plane. On the basis of the parameterized nonlinear model, a distortion-invariant fragment-based joint-feature mixture model of Gaussian is presented for human target tracking in omnidirectional vision. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into the feature clustering. The joint probability distribution of a joint-feature class is modeled by a mixture of Gaussian. A weight contribution mechanism is designed to flexibly weight the fragments contribution based on their responses, which leads to a robust tracking even under serious partial occlusion. Finally, experiments validate the advantage of the proposed algorithm over other conventional approaches.

Foreground Detection in Surveillance Videos Via a Hybrid Local Texture Based Method

Abstract Foreground detection is a basic but challenging task in computer vision. In this paper, a novel hybrid local texture based method is presented to model the background for complex scenarios and an image segmentation based denoising processing is applied to reduce noise. We combine the uniform pattern of eXtended Center-Symmetric Local Binary Pattern (XCS-LBP) and Center- Symmetric Local Derivative Pattern (CS-LDP) to generate a discriminative feature with shorter histogram. Retaining the strengths of the two textures, it appears to be robust to dynamic scenes, illumination changes and noise. Based on the hybrid feature, we employ an overlapping block based Gaussian Mixture Model (GMM) framework which makes classifying decision in pixel level. Experimental results on two changeling datasets (Wallflower and I2R dataset) clearly justify the performance of proposed method. Besides, we take the foreground masks obtained by proposed method as input to a tracking system showing notable results.

Small-parallel exemplar-based voice conversion in noisy environments using affine non-negative matrix factorization

The need to have a large amount of parallel data is a large hurdle for the practical use of voice conversion (VC). This paper presents a novel framework of exemplar-based VC that only requires a small number of parallel exemplars. In our previous work, a VC technique using non-negative matrix factorization (NMF) for noisy environments was proposed. This method requires parallel exemplars (which consist of the source exemplars and target exemplars that have the same texts uttered by the source and target speakers) for dictionary construction. In the framework of conventional Gaussian mixture model (GMM)-based VC, some approaches that do not need parallel exemplars have been proposed. However, in the framework of exemplar-based VC for noisy environments, such a method has never been proposed. In this paper, an adaptation matrix in an NMF framework is introduced to adapt the source dictionary to the target dictionary. This adaptation matrix is estimated using only a small parallel speech corpus. We refer to this method as affine NMF, and the effectiveness of this method has been confirmed by comparing its effectiveness with that of a conventional NMF-based method and a GMM-based method in noisy environments.

Cochannel Speaker Identification in Anechoic and Reverberant Conditions

Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We first investigate GMM-based approaches and propose a combined system that integrates two cochannel SID methods. Second, we explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate that our proposed systems significantly improve SID performance over recent approaches in both anechoic and reverberant conditions and various target-to-interferer ratios.

Generative part-based Gabor object detector

Discriminative part-based models have become the approach for visual object detection. The models learn from a large number of positive and negative examples with annotated class labels and location (bounding box). In contrast, we propose a part-based generative model that learns from a small number of positive examples. This is achieved by utilizing “privileged information”, sparse class-specific landmarks with semantic meaning. Our method uses bio-inspired complex-valued Gabor features to describe local parts. Gabor features are transformed to part probabilities by unsupervised Gaussian Mixture Model (GMM). GMM estimation is robustified for a small amount of data by a randomization procedure inspired by random forests. The GMM framework is also used to construct a probabilistic spatial model of part configurations. Our detector is invariant to translation, rotation and scaling. On part level invariance is achieved by pose quantization which is more efficient than previously proposed feature transformations. In the spatial model, invariance is achieved by mapping parts to an “aligned object space”. Using a small number of positive examples our generative method performs comparably to the state-of-the-art discriminative method.