This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as ...

Dec 1, 2017 · The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second ...

Dec 1, 2017 · The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised ...

Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning · A. Lagrange, M. Fauvel, +1 author. N. Dobigeon · Published in ...

The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering ...

The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering ...

Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform ...

This paper proposes a unified framework to perform classification and low-level modeling jointly. • Robustness is improved by considering a possibly badly ...

This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral ...

Hierarchical Bayesian image analysis: From low-level modeling to robust supervised learning. A Lagrange, M Fauvel, S May, N Dobigeon. Pattern Recognition 85 ...