Robust hierarchical image representation using non-negative matrix factorisation with sparse code shrinkage preprocessing

B. Szatmáry, G. Szirtes, A. Lörincz, J. Eggert, E. Körner

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

When analysing patterns, our goals are (i) to find structure in the presence of noise, (ii) to decompose the observed structure into sub-components, and (iii) to use the components for pattern completion. Here, a novel loop architecture is introduced to perform these tasks in an unsupervised manner. The architecture combines sparse code shrinkage with non-negative matrix factorisation, and blends their favourable properties: sparse code shrinkage aims to remove Gaussian noise in a robust fashion; non-negative matrix factorisation extracts sub-structures from the noise filtered inputs. The loop architecture performs robust pattern completion when organised into a two-layered hierarchy. We demonstrate the power of the proposed architecture on the so-called 'bar-problem' and on the FERET facial database.

Original languageEnglish
Pages (from-to)194-200
Number of pages7
JournalPattern Analysis and Applications
Volume6
Issue number3
DOIs
Publication statusPublished - Dec 1 2003

Keywords

  • Hierarchy
  • Non-negative matrix factorisation
  • Sparse code shrinkage

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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