Connecting the Dots: Image Classification via Sparse Representation from a Constrained Subspace Perspective
- 10.2991/msam-18.2018.65How to use a DOI?
- sparse representation; constrained subspace; manifold approximation
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.
- © 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 - 10.2991/msam-18.2018.65 ID - Liao2018/07 ER -