We present a Gaussian Mixture model for detecting different types of figurative language in context. We show that this model performs well when the parameters are estimated in an ...
The expectation maximization (EM) algorithm is widely used in the Gaussian mixture model (GMM) as the state-of-art statistical modeling technique. Like the classical EM method, th...
Sheeraz Memon, Margaret Lech, Namunu Chinthaka Mad...
In confocal microscopy imaging, target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and ...
Kangyu Pan, Anil C. Kokaram, Jens Hillebrand, Mani...
Instead of traditional ways of creating road maps, an attractive alternative is to create a map based on GPS traces of regular drivers. One important aspect of this approach is to...
—A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approac...
Stephen J. Roberts, Dirk Husmeier, Iead Rezek, Wil...
Abstract. Influence of projection pursuit on classification errors and estimates of a posteriori probabilities from the sample is considered. Observed random variable is supposed t...
We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio c...
Douglas Turnbull, Luke Barrington, D. Torres, Gert...
We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture m...
To track objects in video sequences, many studies have been done to characterize the target with respect to its color distribution. Most often, the Gaussian Mixture Model (GMM) is ...
We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector s...