Abstract We propose a greedy variational method for decomposing non-negative multivariate signal as weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to Gaussian mixture model. Notably, our has following features: (1) It accepts signals, i.e., sampled functions, histograms, time series, images, etc., input. (2) The can handle general (i.e., ellipsoidal) Gauss...

In most Information Retrieval (IR) applications, Euclidean distance is used for similarity measurement. It is adequate in many cases but this distance metric is not very accurate when there exist some different local data distributions in the database. We propose a Gaussian mixture distance for performing accurate nearest-neighbor search for Information Retrieval (IR). Under an established Gaus...

Gaussian mixture background model is widely used in moving target detection of the image sequences. However, traditional Gaussian mixture background model usually considers the time continuity of the pixels, and establishes background through statistical distribution of pixels without taking into account the pixels’ spatial similarity, which will cause noise, imperfection and other problems. Th...

Abstract: In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then,to fit the Gaussian mixtures to the histogram of image, the Expectation Maximization (EM) algorithm is developed to estimate the number of Gaussian mixt...

Figure 1.1: Visualization of Tensors of different orders. gained popularity in parameter estimation for a variety of problems. In this lecture, the focus is on how they may be used in estimating the parameters of Gaussian Mixture Models and Hidden Markov Models. In a Gaussian mixture model, there are k unknown n-dimensional multivariate Gaussian distributions. Samples are generated by first pic...