Image Classification Using Sparse Coding and Spatial Pyramid Matching

DOI

10.2991/iceeim-14.2014.23How to use a DOI?

Keywords

Sparse coding, Spatial pyramid matching, Support vector machine, Image classification

Abstract

Recently, the Support Vector Machine (SVM) using Spatial Pyramid Matching (SPM) kernel has achieved remarkable successful in image classification. The classification accuracy can be improved further when combining the sparse coding with SPM. However, the existing methods give the same weight of patches of SPM at different levels. Clearly the discriminative powers of SPM at different levels are distinct and there are correlation relationships among the sparse coding bases vectors, which usually have negative influence on the classification accuracy. This paper assigns different weights to the patches at different levels of SPM, and then proposes a new spatial pyramid matching kernel. Furthermore, the Principle Component Analysis (PCA) is employed to reduce the dimension of the feature vectors in order to decrease correlation among vectors and speed up the SVM training process. The preprocessing can enhance the discriminative ability of the new kernel as well. Experiments carried out on Caltech101 and Caltech256 datasets show that the new SPM kernel outperforms the existing methods in terms of the classification accuracy.

Copyright

© 2014, 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  - Xiaofang Wang
AU  - Jun Ma
AU  - Ming Xu
PY  - 2014/04
DA  - 2014/04
TI  - Image Classification Using Sparse Coding and Spatial Pyramid Matching
BT  - Proceedings of the 2014 International Conference on e-Education, e-Business and Information Management
PB  - Atlantis Press
SP  - 81
EP  - 84
SN  - 1951-6851
UR  - https://doi.org/10.2991/iceeim-14.2014.23
DO  - 10.2991/iceeim-14.2014.23
ID  - Wang2014/04
ER  -