Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generative/Discriminative Learning Algorithm for Image Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Constrained spectral clustering via exhaustive and efficient constraint propagation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
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In Gaussian mixture modeling, it is crucial to select the number of Gaussians for a sample set, which becomes much more difficult when the overlap in the mixture is larger. Under regularization theory, we aim to solve this problem using a semi-supervised learning algorithm through incorporating pairwise constraints into entropy regularized likelihood (ERL) learning which can make automatic model selection for Gaussian mixture. The simulation experiments further demonstrate that the presented semi-supervised learning algorithm (i.e., the constrained ERL learning algorithm) can automatically detect the number of Gaussians with a good parameter estimation, even when two or more actual Gaussians in the mixture are overlapped at a high degree. Moreover, the constrained ERL learning algorithm leads to some promising results when applied to iris data classification and image database categorization.