21 June 2018

Séminaire pôle Signal et Image : Consistent sequential dictionary learning

Ce séminaire sera animé par Karim Seghouane chercheur à l’université de Melbourne, Australie Algorithms for learning overcomplete dictionaries for sparse signal representation are mostly iterative minimization methods. Such algorithms (including the popular K-SVD) alternate between a sparse coding stage and a dictionary update stage. For most, however, the notion of consistency of the learned quantities has not been addressed. As an example, the non-consistency of the dictionary learned by K-SVD will be discussed in this presentation. New adaptive dictionary learning algorithms are presented based on the observation that the observed signals can be approximated as a sum of matrices of the same or different ranks. The proposed methods are derived via sequential penalized rank one or K matrix approximation, where a sparisity norm is introduced as a penalty that promotes sparsity. The proposed algorithms use block coordinate descent approach to consistently estimate the unknowns and have the advantage of having simple closed form solutions for both sparse coding and dictionary update stages. The consistency properties of both the estimated sparse code and dictionary atom are discussed. Experimental results are presented on simulated data and on a real functional magnetic resonance imaging (fMRI) dataset from a finger tapping experiment. Results illustrate the performance improvement of the proposed algorithm compared to other existing algorithms. Applications: Dictionary learning is a widely used approach in image processing, medical imaging and computer vision problems. Très cordialement
21 June 2018, 14h0016h00
Marseille Lmuniy

salle doc bâtiment A de polytech
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