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Publications of J.M. Francos
Result of the query in the list of publications :
Article |
1 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation.
H. Permuter and J.M. Francos and I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, April 2006.
Keywords : Classification, Segmentation, Texture, Colour, Gaussian mixture, 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 |
= |
{April}, |
| 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, Colour, Gaussian mixture, 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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3 Conference articles |
1 - Gaussian Mixture Models of Texture and Colour for Image Database Retrieval.
H. Permuter and J.M. Francos and I. H. Jermyn. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, April 2003.
Keywords : Texture, Gaussian mixture, 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 |
= |
{April}, |
| 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, Gaussian mixture, 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. |
|
2 - Image Retrieval and Indexing: A Hierarchical Approach in Computing the Distance between Textured Images.
R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Image Processing (ICIP), Chicago, USA, October 1998.
@INPROCEEDINGS{stoica98a,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{Image Retrieval and Indexing: A Hierarchical Approach in Computing the Distance between Textured Images}, |
| year |
= |
{1998}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=723675}, |
| keyword |
= |
{} |
| } |
|
3 - The two-dimensional Wold decomposition for segmentation and indexing in image libraries.
R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, May 1998.
@INPROCEEDINGS{stoica98b,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{The two-dimensional Wold decomposition for segmentation and indexing in image libraries}, |
| year |
= |
{1998}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Seattle, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=678151}, |
| keyword |
= |
{} |
| } |
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Technical and Research Report |
1 - Indexing and retrieval in multimedia libraries through parametric texture modeling using the 2D Wold decomposition.
R. Stoica and J. Zerubia and J.M. Francos. Research Report 3594, Inria, December 1998.
Keywords : Markov Fields, Texture, Segmentation, Indexation.
@TECHREPORT{stoica98,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{Indexing and retrieval in multimedia libraries through parametric texture modeling using the 2D Wold decomposition}, |
| year |
= |
{1998}, |
| month |
= |
{December}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3594}, |
| url |
= |
{https://hal.inria.fr/inria-00073085}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73085/filename/RR-3594.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/30/85/PS/RR-3594.ps}, |
| keyword |
= |
{Markov Fields, Texture, Segmentation, Indexation} |
| } |
Résumé :
Ce rapport présente une méthode paramétrique permettant de faire de l'indexati- on et de la recherche dans une base de données multimédia. L'indexation (étiquetage) et la recherche de données multimédia sont réalisées grâce à la modélisation paramétrique de textures qui se trouvent dans les images de la base de données. Les textures sont caracterisées par des paramètres qui servent d'indices pour la recherche dans la base de données. Afin de pouvoir identifier les différentes régions texturées d'une image et estimer les paramètres correspondants, un algorithme de segmentation-estimatio- n est proposé dans ce rapport, qui fait appel à une décomposition de Wold 2D pour le modèle de texture et à un modèle markovien pour l'étiquetage. L'indexation nécessite de définir une distance entre les images. Une nouvelle distance, inspirée de la distance de Kullback, est décrite dans ce rapport. Elle utilise les paramètres estimés correspondants au modèle 2D de chaque texture. Les résultats obtenus relativement à la segmentation et à l'indexatio- n sont proches de ceux obtenus par un opérateur humain. |
Abstract :
This paper presents a parametric method for indexing and retrieval of multimedia data in digital libraries. %Indexing (labeling) and retrieval %of multimedia data, based on the properties %of the imagery components of the stored data record, are derived. Indexing (labeling) and retrieval of the multimedia data are performed using parametric modeling of the textured segments found in the data imagery components. The estimated parametric models of the textured segments serve as their indices, and hence as indices of the entire image, as well as of the multimedia record which the image is part thereof. To achieve the ability to identify textured image regions and estimate their parameters, a joint segmentation-estimation algorithm that combines the 2-D Wold decomposition based texture model with a Markovian labeling process, is derived. Ordering and indexing of images require a definition of a distance measure between images. Using the framework of the Kullback distance between probability distributions, a new rigorous distance measure between textures is derived. The distance between any two textured image segments is evaluated using their estimated parametric models. The proposed segmentation, distance evaluation, and indexing methods are shown to produce comparable results to those obtained by a human viewer. |
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