in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...

We present a comparison of speaker verification systems based on unsupervised and supervised mixtures of probabilistic linear discriminant analysis (PLDA) models. This paper explores current applicability of unsupervised mixtures of PLDA models with Gaussian priors in a total variability space for speaker verification. Moreover, we analyze the experimental conditions under which this applicatio...

We discover a strong relation between two known learning models: stream-based active learning and perfect selective classification (an extreme case of ‘classification with a reject option’). For these models, restricted to the realizable case, we show a reduction of active learning to selective classification that preserves fast rates. Applying this reduction to recent results for selective cla...

Knowing whether a household behaves according to separability or non-separability is needed for the correct modeling of production decisions. We propose a superior test to those found in the literature on separability by using a mixture distribution approach to estimate the probability that a farm household behaves according to non-separability, and test that the determinants of consumption aff...

The non-linear decision boundary between object and background classes due to large intra-class variations needs to be modelled by any classifier wishing to achieve good results. While a mixture of linear classifiers is capable of modelling this non-linearity, learning this mixture from weakly annotated data is non-trivial and is the paper’s focus. Our approach is to identify the modes in the d...

In perceptive interface technologies used in smart-room environments, the determination of speech activity is one of key objectives. Due to the presence of environmental noises and reverberation, a robust Speech Activity Detection (SAD) system is required. In a previous work, a SAD system, which used Linear Discriminant Analysis-extracted features and a Decision Tree classifier, was successfull...

We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes. The model parameters are learned by maximizing the observed-data log-likelihood for each class by using a dedicated expectation-maximizatio...

Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance ...

In this paper, two novel features, Line Spectrum Center Range and Line Spectrum Flux, both derived from Line Spectrum Frequencies, are proposed to detect the presence of speech in various acoustic environments. Evaluation results using Fischer Discriminant Analysis and Scatter Matrices indicated that the new features excel the state-of-theart features. An environmental robust hybrid feature set...

Clustering mixtures of Gaussian distributions is a fundamental and challenging problem that is ubiquitous in various high-dimensional data processing tasks. While state-of-the-art work on learning Gaussian mixture models has focused primarily on improving separation bounds and their generalization to arbitrary classes of mixture models, less emphasis has been paid to practical computational eff...