nowadays, audio and video media data is already facilitates generation, transmission, storage and circulation on the global scale. Audio and video data is geometrically fast as the rate of growth, the video data processing and analysis have lagged behind the pace of development in the growth of data, resulting in large amounts of data is wasted. Therefore, it becomes an urgent need for efficien...

Despite possible structural changes related to atrophy and edema, the structural anatomy of the brain should present time consistency for a given patient. Based on this assumption, we propose a lesion segmentation method that first derives a gaussian mixture model (GMM) separating healthy tissues from pathological and unexpected ones on a multi-time-point intra-subject groupwise image. This ave...

The aim of this section is to develop a high-performance semantic indexing system using Gaussian mixture model (GMM) supervectors and tree-structured GMMs [1, 2]. GMM spervectors corresponding to six types of audio and visual features are extracted from video shots by using tree-structured GMMs. The computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parameters are re...

The purpose of this paper is to present a novel approach to the Gaussian mixture background modeling model (GMM) that we call the median mixture model (MMM). The proposed method is based on the same principles as the GMM, but all of the background model parameters are estimated in a much more efficient way resulting in accelerating the algorithm by about 25% without deteriorating the modeling r...

We are using Gaussian Mixture Models (GMM) as a tool to construct local mappings of nonlinear Multi-Input Multi-Output (MIMO) systems. In this work we combine the advantages of GMM with the Kalman filter. To improve the accuracy of the local linear mappings in a potentially large dimensional state space, we propose to initialize the GMM parameters with Vector Quantization (VQ) or its more parsi...

This paper describes a feature extraction technique based on fitting a Gaussian mixture model (GMM) to the speech spectral envelope. The features obtained (the component means, variances and priors) represent both the the general shape of the spectrum and provide information on the position of the spectral peaks. As the features select peaks in the spectrum they are related to the formant ampli...

This paper shows that Hidden Markov Models (HMMs) can be effectively applied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian Mixture Model (GMM) technique. Experiments conducted on the Face Recognition Grand Challenge database show that the Equal Error Rate can be reduced from 0.88% for the GMM technique to 0.36% for the best HMM approach.

Most of current state-of-the-art speaker verification (SV) systems use Gaussian mixture model (GMM) to represent the universal background model (UBM) and the speaker models (SM). For an SV system that employs log-likelihood ratio between SM and UBM to make the decision, its computational efficiency is largely determined by the GMM computation. This paper attempts to speedup GMM computation by c...

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appea...

Currently, approach of Gaussian Mixture Model combined with Support Vector Machine to text-independent speaker verification task has produced the stat-of-the-art performance. Many kernels have been reported for combining GMM and SVM. In this paper, we propose a novel kernel to represent the GMM distribution by Taylor expansion theorem and it’s regarded as the input of SVM. The utterance-specifi...