Multi-layer Dictionary Learning for Image Classification

  • Stefen Chan Wai Tim
  • Michele Rombaut
  • Denis Pellerin
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

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.

Keywords

Input Image Sparse Representation Sparse Code Convolutional Neural Network Dictionary Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgement

This work has been partially supported by the LabEx PERSYVAL-Lab (ANR-11-LABX-0025-01).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Stefen Chan Wai Tim
    • 1
  • Michele Rombaut
    • 1
  • Denis Pellerin
    • 1
  1. 1.GIPSA-LabUniversity of Grenoble AlpesGrenobleFrance

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