1 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation.
H. Permuter et J.M. Francos et I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, avril 2006.
Mots-clés : Classification, Segmentation, Texture, Couleur, Mixture de gaussiennes, Decison fusion.
@ARTICLE{permuter_pr06,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, |
| year |
= |
{2006}, |
| month |
= |
{avril}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{39}, |
| number |
= |
{4}, |
| pages |
= |
{695--706}, |
| url |
= |
{http://dx.doi.org/10.1016/j.patcog.2005.10.028}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_permuter_pr06.pdf}, |
| keyword |
= |
{Classification, Segmentation, Texture, Couleur, Mixture de gaussiennes, Decison fusion} |
| } |
Abstract :
The aims of this paper are two-fold: to define Gaussian mixture models of coloured texture on several feature paces 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 Gaussian mixtures models over a variety of different colour and texture feature spaces, with a view to the retrieval of textured colour images from databases. We compare supervised classification results for different choices of colour 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 'colour' and 'structure' information in order to improve the classification performance. 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. |
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