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

PATTERN RECOGNITION

卷:39

Article

Permuter, H ; Francos, J ; Jermyn, I

关键词: image classification;    image segmentation;    texture;    color;    Gaussian mixture models;    expectation maximization;    k-means;    background inodel;    decision fusion;    aerial images;   

DOI  :  10.1016/j.patcog.2005.10.028

来源: Elsevier

【 摘 要 】

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. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

【 授权许可】

Free