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Modélisation multivariée de champs texturaux : application à la classification d'images.

Abstract : The prime objective of this thesis is to propose an unsupervised classification algorithm of textured images based on multivariate stochastic models. Inspired from classification algorithm named "Bag of Words" (BoW), we propose an original extension to parametric descriptors issued from the multivariate modeling of wavelet subband coefficients. Some major contributions of this thesis can be outlined. The first one concerns the introduction of an intrinsic prior on the parameter space by defining a Gaussian concentrated distribution. This latter being characterized by a centroid ¯_ and a variance _2,we propose an estimation algorithm for those two quantities. Next, we propose an application to the multivariate SIRV (Spherically Invariant Random Vector) model, by resolving the difficult centroid estimation problem as the solution of two simpler ones (one for the Gaussian part and one for the multiplier part). To handle with the intra-class diversity of texture images (scene enlightenment, orientation . . . ), we propose an extension to mixture models allowing the construction of the training dictionary. Finally, we validate this classification algorithm on various texture image databases and show interesting classification performances compared to other state-of-the-art algorithms.
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Submitted on : Tuesday, November 15, 2016 - 11:29:07 AM
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  • HAL Id : tel-01396943, version 1


Aurélien Schutz. Modélisation multivariée de champs texturaux : application à la classification d'images.. Autre. Université de Bordeaux, 2014. Français. ⟨NNT : 2014BORD0356⟩. ⟨tel-01396943⟩



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