Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Feature and Model Selection for Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying critical variables of principal components for unsupervised feature selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
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We propose a target segmentation approach based on sensor data fusion that can deal with the problem of a diverse background. Features from sensor images, including data from a laser scanner and passive sensors (cameras), are analyzed using Gaussian mixture estimation. The approach tackles some of the difficulties with Gaussian mixtures, e.g., selecting the number of initial components and a good description of data in terms of the number of Gaussian components, and determining the relevant features for the current data set. The feature selection quality is analyzed on-line. We propose a criterion that determines the quality of the resulting clusters in terms of their respective spatial distribution. The output from the analysis is used for object-background segmentation. Segmentation examples of surface-laid mines in outdoor scenes are shown.