Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contai...
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ISBN:
(纸本)9781538604571
Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains about 30000 facial images from thousands of individuals. Each image has been individually labeled about 40 times, then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study shows that the action units of basic emotions in RAF-DB are much more diverse than, or even deviate from, those of lab-controlled ones. To address this problem, we propose a new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters. The benchmark experiments on the 7-class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+ databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning based methods for the expression recognition in the wild.
This paper considers the statistical approach to model software degradation process from time series data of system attributes. We first develop the continuous-time Markov chain (CTMC) model to represent the degradati...
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ISBN:
(纸本)9781538616796
This paper considers the statistical approach to model software degradation process from time series data of system attributes. We first develop the continuous-time Markov chain (CTMC) model to represent the degradation level of system. By combining the CTMC with system attributes distributions, a continuous-time hidden Markov model (CT-HMM) is proposed as the basic model to represent the degradation level of system. To estimate model parameters, we develop the EM algorithm for CT-HMM. The advantage of this modeling is that the estimated model is directly applied to existing CTMC-based software aging and rejuvenation models. In numerical experiments, we exhibit the performance of our method by simulated data and also demonstrate estimating the software degradation process with experimental data in MySQL database system.
To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature ...
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To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U-LBSP (uniform-local binary similarity patterns) texture feature, lab colour and location feature are used to construct local fusion feature. U-LBSP is modified from local binary patterns in order to reduce computational complexity and better resist the influence of shadow and illumination changes. Joint colour and location feature are introduced to deal with the problem of indigent texture and scenario jitter. Then, LFGMM (Gaussian mixture model based on local fusion feature) is updated and learned by variational Bayes. In order to adapt to dynamic changing scenarios, the variational expectationmaximisationalgorithm is applied for distribution parameters optimisation. In this way, the optimal number of Gaussian components as well as their parameters can be automatically estimated with less time expended. Experimental results show that the authors' method achieves outstanding detection performance especially under conditions of shadow disturbances, illumination changes, indigent texture and scenario jitter. Strong robustness and high accuracy have been achieved.
In this article, we address the problem of clustering imprecise data using a finite mixture of Gaussians. We propose to estimate the parameters of the model using the fuzzy EM algorithm. This extension of the EM algor...
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In this article, we address the problem of clustering imprecise data using a finite mixture of Gaussians. We propose to estimate the parameters of the model using the fuzzy EM algorithm. This extension of the EM algorithm allows us to handle imprecise data represented by fuzzy numbers. First, we briefly recall the principle of the fuzzy EM algorithm. Then, we provide closed-forms for the parameter estimates in the case of Gaussian fuzzy data. We also describe a Monte-Carlo procedure for estimating the parameter updates in the general case. Experiments carried out on synthetic and real data demonstrate the interest of our approach for taking into account attribute and label uncertainty. (C) 2015 Elsevier B.V. All rights reserved.
A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a sin...
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A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proved using the expectation-maximization algorithm. In addition, a measure to evaluate the goodness of fit of the resulting factorization is described. The proposed methods allow modeling of nonlinear effects using appropriate link functions and are illustrated using an application in biomedical signal processing.
The authors present a novel cardinalised probability hypothesis density (CPHD) algorithm under the assumption of a glint measurement noise model with unknown inverse covariance. Noise parameters are assumed to have a ...
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The authors present a novel cardinalised probability hypothesis density (CPHD) algorithm under the assumption of a glint measurement noise model with unknown inverse covariance. Noise parameters are assumed to have a Gamma prior distribution so that the predicted and updated PHDs can have mixture of Gaussians representations. A variational Bayesian expectationmaximisation procedure is applied to iteratively estimate parameters of the mixture distributions through random hypersurface model CPHD prediction and update steps. Simulation results show that the proposed algorithm can adaptively track extended objects with unknown object number and glint measurement noise, while achieving higher precision compared against the traditional approach.
In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probabil...
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In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectationmaximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model.
In this study, the authors propose a lattice reduction (LR)-based doubly iterative receiver for joint channel estimation and detection in multiple-input-multiple-output (MIMO) bit-interleaved coded modulation systems....
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In this study, the authors propose a lattice reduction (LR)-based doubly iterative receiver for joint channel estimation and detection in multiple-input-multiple-output (MIMO) bit-interleaved coded modulation systems. For the inner iteration loop of the receiver, LR-based randomised sampling detection is employed to enjoy the tradeoff between performance and complexity while for the outer iteration loop, the expectation-maximisation (EM)-based iterative channel estimation using sampling results is proposed to achieve the maximum likelihood channel estimation performance. Besides, a modified computational efficient EM-based channel estimation approach is also derived to reduce the complexity further. Simulation results demonstrate that the proposed doubly MIMO iterative receiver can have comparable bit-error rate performance with a reasonable computational complexity.
This paper presents insights on the promises of probabilistic modeling and machine learning for fault diagnosis in optical access networks. A Bayesian inference engine, called Probabilistic tool for GPON-FTTH Access N...
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ISBN:
(纸本)9781509040063
This paper presents insights on the promises of probabilistic modeling and machine learning for fault diagnosis in optical access networks. A Bayesian inference engine, called Probabilistic tool for GPON-FTTH Access Network self-DiAgnosis (PANDA), is applied to fault diagnosis of Gigabit capable Passive Optical Networks (GPON). PANDA approach has been assessed on real diagnosis data, showing very satisfactory alignment with an operational rule-based expert system. Furthermore, it provides diagnosis conclusions for all tested cases, even if some monitoring data is missing or incomplete. Finally, an expectation maximization algorithm allows to finely tune the probabilistic model.
Many signal and image processing applications, including SAR polarimetry and texture analysis, require the classification of complex covariance matrices. The present paper introduces a geometric learning approach on t...
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ISBN:
(纸本)9781509041183
Many signal and image processing applications, including SAR polarimetry and texture analysis, require the classification of complex covariance matrices. The present paper introduces a geometric learning approach on the space of complex covariance matrices based on a new distribution called Riemannian Gaussian distribution. The proposed distribution has two parameters, the centre of mass Y and the dispersion parameter σ. After having derived its maximum likelihood estimator and its extension to mixture models, we propose an application to texture recognition on the VisTex database.
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