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

Research output: Contribution to journalArticlepeer-review

247 Scopus citations

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.

Original languageEnglish
Pages (from-to)695-706
Number of pages12
JournalPattern Recognition
Volume39
Issue number4
DOIs
StatePublished - 1 Apr 2006

Keywords

  • Aerial images
  • Background model
  • Color
  • Decision fusion
  • Expectation maximization
  • Gaussian mixture models
  • Image classification
  • Image segmentation
  • Texture
  • k-means

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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