Markov Random Fields for Recognizing textures modeled by Feature Vectors

Juliette Blanchet 1, 2 Florence Forbes 2 Cordelia Schmid 1, *
* Auteur correspondant
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This paper decribes a new probabilistic framework for recognizing textures in images. Images are described by local affine-invariant descriptors and by spatial relationships between these descriptors. We propose to introduce the use of statistical parametric models of the dependence between descriptors. Hidden Markov Models (HMM) are investigated for such a task using recent estimation procedures based on the mean eld principle to perform the non trivial parameter estimation they require. Preliminary experiments obtained with 140 images of seven dierent natural textures show promising results.
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Communication dans un congrès
International Conference on Applied Stochastic Models and Data Analysis (ASMDA '05), May 2005, Brest, France. pp.1161--1169, 2005, 〈http://conferences.telecom-bretagne.eu/asmda2005/article8fd3.html?id_article=37〉
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Juliette Blanchet, Florence Forbes, Cordelia Schmid. Markov Random Fields for Recognizing textures modeled by Feature Vectors. International Conference on Applied Stochastic Models and Data Analysis (ASMDA '05), May 2005, Brest, France. pp.1161--1169, 2005, 〈http://conferences.telecom-bretagne.eu/asmda2005/article8fd3.html?id_article=37〉. 〈inria-00548518〉

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