Multi-layer Dictionary Learning for Image Classification

Stefen Chan Wai Tim (1) , Michèle Rombaut (1) , Denis Pellerin (1)

1 GIPSA-AGPIG - GIPSA - Architecture, Géométrie, Perception, Images, Gestes

This paper presents a multi-layer dictionary learning method for classification tasks. The goal of the proposed multi-layer framework is to use the supervised dictionary learning approach locally on raw images in order to learn local features. This method starts by building a sparse representation at the patch-level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. This method has been tested on a classification task with good results.

Mots clés: Classification; Dictionary; Multi-layer architecture

Domaines: Traitement du signal et de l'image [eess.SP]

Stefen Chan Wai Tim, Michèle Rombaut, Denis Pellerin. Multi-layer Dictionary Learning for Image Classification. ACIVS 2016 - International Conference on Advanced Concepts for Intelligent Vision Systems, Oct 2016, Lecce, Italy. ⟨hal-01388907⟩