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Semisupervised segmentation of remote sensing images with active queries.

Publications associées (49)

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Automatic generation of tourist maps

Mark Pauly

Tourist maps are essential resources for visitors to an unfamiliar city because they visually highlight landmarks and other points of interest. Yet, hand-designed maps are static representations that cannot adapt to the needs and tastes of the individual t ...

2008

Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images

Jean-Philippe Thiran, Meritxell Bach Cuadra, Leila Cammoun, Olivier Cuisenaire, Torsten Butz

This paper presents a validation study on statistical non-supervised brain tissue classification techniques in MR images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of cla ...

2005

Region-based Satellite Image Classification: Method and Validation

Jean-Philippe Thiran, Meritxell Bach Cuadra, Leila Cammoun, Xavier Gigandet, Abram Pointet

We propose an algorithm for very high-resolution satellite image classification that combines non-supervised segmentation with a supervised classification. Both multi-spectral data and local spatial priors are used in the Gaussian Hidden Markov Random Fiel ...

EUSIPCO2005

Fast Sparse Gaussian Process Methods: The Informative Vector Machine

Matthias Seeger

We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on information-theoretic principles, previously suggested for active learning. Our goal is not only to learn d-sparse predictors (which can be ...

2003

Evaluation of SVM Binary Classification with Nonparametric Stochastic Simulations

The quality of Support Vector Machines binary classification of spatial environmental data is evaluated with geostatistical nonparametrtic conditional stochastic simulations. Equally probable realizations are generated and compared with SVM. Case study is ...

idiap2001

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000

GIS Databases: From Multiscale toMultiRepresentation

Stefano Spaccapietra, Christine Parent, Christelle Vangenot

Cartography is one of the major application areas using geographical databases. Whether it is for the business of producing paper maps for sale, or whether it is for displaying maps on a screen to visualize the result of a query, we need computer systems t ...

Springer2000