A Fast Globally Supervised Learning Algorithm for Gaussian Mixture Models.
ABSTRACT In this paper, a fast globally supervised learning algorithm for Gaussian Mixture Models based on the maximum relative entropy
(MRE) is proposed. To reduce the computation complexity in Gaussian component probability densities, the concept of quasi-Gaussian
probability density is used to compute the simplified probabilities. For four different learning algorithms such as the maximum
mutual information algorithm (MMI), the maximum likelihood estimation (MLE), the generalized probabilistic descent (GPD) and
the maximum relative entropy (MRE) algorithm, the random experiment approach is used to evaluate their performances. The experimental
results show that the MRE is a better alternative algorithm in accuracy and training speed compared with GPD, MMI and MLE.
- SourceAvailable from: Élisa FromontPattern Recognition 02/2012; 45:897-907. DOI:10.1016/j.patcog.2011.07.021 · 2.58 Impact Factor
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ABSTRACT: Persistence of objects in scenes is an important parameter of video object tracking systems. From the analysis of objects' durations (of stay) we not only get how long they stay in the scene, but also precisely where the objects spend time. The video frame is therefore segmented into clusters, and objects which go through or stay there are assigned to that cluster. If we observe all objects in a time period we should get a model of object behavior with respect to duration for each cluster. Using the built model we try to find abnormal object behavior. To build a model of object's spatial duration from the video data we utilize Gaussians and fast learning algorithm for real time surveillance applications on embedded systems.Human System Interactions, 2009. HSI '09. 2nd Conference on; 06/2009