A study of Gaussian mixture models of color and texture features for image classification and segmentation

Permuter, H.; Francos, J.; Jermyn, I.H.

doi:10.1016/j.patcog.2005.10.028

A study of Gaussian mixture models of color and texture features for image classification and segmentation

Permuter, H.; Francos, J.; Jermyn, I.H.

Authors

H. Permuter

J. Francos

Professor Ian Jermyn i.h.jermyn@durham.ac.uk
Professor

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.