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Publications sur Mixture de gaussiennes
Résultat de la recherche dans la liste des publications :
Article |
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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2 Articles de conférence |
1 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients.
R. Cossu et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, mai 2004.
Mots-clés : Bimodale, Adaptatif, Paquet d'ondelettes, Texture, Mixture de gaussiennes, Statistics.
@INPROCEEDINGS{cossu04a,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients}, |
| year |
= |
{2004}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Montreal, Canada}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icassp.pdf}, |
| keyword |
= |
{Bimodale, Adaptatif, Paquet d'ondelettes, Texture, Mixture de gaussiennes, Statistics} |
| } |
Abstract :
Although subband histograms of the wavelet coefficients of
natural images possess a characteristic leptokurtotic form,
this is no longer true for wavelet packet bases adapted to
a given texture. Instead, three types of subband statistics
are observed: Gaussian, leptokurtotic, and interestingly, in
some subbands, multimodal histograms. These subbands
are closely linked to the structure of the texture, and guarantee
that the most probable image is not flat. Motivated by
these observations, we propose a probabilistic model that
takes them into account. Adaptive wavelet packet subbands
are modelled as Gaussian, generalized Gaussian, or a constrained
Gaussian mixture. We use a Bayesian methodology,
finding MAP estimates for the adaptive basis, for subband
model selection, and for subband model parameters.
Results confirm the effectiveness of the proposed approach,
and highlight the importance of multimodal subbands for
texture discrimination and modelling. |
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2 - Gaussian Mixture Models of Texture and Colour for Image Database Retrieval.
H. Permuter et J.M. Francos et I. H. Jermyn. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, avril 2003.
Mots-clés : Texture, Mixture de gaussiennes, Classification, Aerial images.
@INPROCEEDINGS{Permuter03,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{Gaussian Mixture Models of Texture and Colour for Image Database Retrieval}, |
| year |
= |
{2003}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Hong Kong}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Permuter03icassp.pdf}, |
| keyword |
= |
{Texture, Mixture de gaussiennes, Classification, Aerial images} |
| } |
Abstract :
We introduce Gaussian mixture models of ‘structure’ and
colour features in order to classify coloured textures in images,
with a view to the retrieval of textured colour images
from databases. Classifications are performed separately
using structure and colour and then combined using
a confidence criterion. We apply the models to the VisTex
database and to the classification of man-made and natural
areas in aerial images. We compare these models with others
in the literature, and show an overall improvement in
performance. |
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