Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)

Connecting the Dots: Image Classification via Sparse Representation from a Constrained Subspace Perspective

Authors
Liang Liao, Stephen John Maybank, Haichang Ye, Xin Liu, Xinqiang Wang
Corresponding Author
Liang Liao
Available Online July 2018.
DOI
https://doi.org/10.2991/msam-18.2018.65How to use a DOI?
Keywords
sparse representation; constrained subspace; manifold approximation
Abstract

We consider the problem of classifier design via sparse representation based on a constrained subspace model. We argue that the data points in the linear span of the training samples should be constrained in order to yield a more accurate approximation to the corresponding data manifold. For this purpose, the constrained set of data points is formulated as a union of affine subspaces in the form of affine hulls spanned by training samples. We argue that the intrinsic dimension of the affine subspaces should be equal to that of data manifold. Thus, a classifier based on this model has a high classification accuracy similar to that of the conceptual NM (Nearest Manifold) classifier. Based on this model, we connect the dots of some classical classifiers including NN (Nearest Neighbor), NFL (Nearest Feature Line), NS (Nearest subspace) and the recently emerged state-of-the-art SRC (Sparse Representation Classifiers) and interpret the mechanism of SRC and Yang's variant of the SRC using the constrained subspace perspective. Experiments on the Extended Yale B database for image classification corroborate our claims and demonstrate the possibility of a proposed classifier called NCSC-CSR which has higher classification accuracy and robustness.

Copyright
© 2018, 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/).

Download article (PDF)

Volume Title
Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
Series
Advances in Intelligent Systems Research
Publication Date
July 2018
ISBN
978-94-6252-566-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-18.2018.65How to use a DOI?
Copyright
© 2018, 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/).

Cite this article

TY  - CONF
AU  - Liang Liao
AU  - Stephen John Maybank
AU  - Haichang Ye
AU  - Xin Liu
AU  - Xinqiang Wang
PY  - 2018/07
DA  - 2018/07
TI  - Connecting the Dots: Image Classification via Sparse Representation from a Constrained Subspace Perspective
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 308
EP  - 315
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.65
DO  - https://doi.org/10.2991/msam-18.2018.65
ID  - Liao2018/07
ER  -