A Fuzzy Adaptive K-SVD Dictionary Algorithm for Face Recogntion

DOI

10.2991/iccsee.2013.544How to use a DOI?

Keywords

Sparse representation, Fuzzy sets, K-SVD, Image recognition

Abstract

Sparse representations using overcomplete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment meaning that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel fuzzy adaptive way to adapting dictionaries in order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by incorporated a new mechanism of fuzzy set, which is called fuzzy K-SVD. Experimental results conducted on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoning Song
AU  - Zi Liu
PY  - 2013/03
DA  - 2013/03
TI  - A Fuzzy Adaptive K-SVD Dictionary Algorithm for Face Recogntion
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2164
EP  - 2168
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.544
DO  - 10.2991/iccsee.2013.544
ID  - Song2013/03
ER  -