H. Permuter
A study of Gaussian mixture models of color and texture features for image classification and segmentation
Permuter, H.; Francos, J.; Jermyn, I.H.
Abstract
The aims of this paper are two-fold: to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct GMMs over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the ‘color’ and ‘structure’ information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.
Citation
Permuter, H., Francos, J., & Jermyn, I. (2006). A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognition, 39(4), 695-706. https://doi.org/10.1016/j.patcog.2005.10.028
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 19, 2005 |
Publication Date | Apr 1, 2006 |
Deposit Date | Aug 12, 2011 |
Publicly Available Date | Jul 31, 2015 |
Journal | Pattern Recognition |
Print ISSN | 0031-3203 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 39 |
Issue | 4 |
Pages | 695-706 |
DOI | https://doi.org/10.1016/j.patcog.2005.10.028 |
Keywords | Image classification, Image segmentation, Texture, Color, Gaussian mixture models, Expectation maximization, k-means, Background model, Decision fusion, Aerial images. |
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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 39, 4, April 2006, 10.1016/j.patcog.2005.10.028.
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