FEATURE EXTRACTION ALGORITHM BASED ON SAMPLE SET RECONSTRUCTION

DOI

10.2991/iccsee.2013.698How to use a DOI?

Keywords

image recognition, transfer learning, sample set reconstruction

Abstract

When the number of labeled training samples is very small, the sample information people can use would be very little and the recognition rates of traditional image recognition methods are not satisfactory. However, there is often some related information contained in other databases that is helpful to feature extraction. Thus, it is considered to take full advantage of the data information in other databases by transfer learning. In this paper, the idea of transferring the samples is employed and further we propose a feature extraction approach based on sample set reconstruction. We realize the approach by reconstructing the training sample set using the difference information among the samples of other databases. Experimental results on three widely used face databases AR FERET CAS-PEAL are presented to demonstrate the efficacy of the proposed approach in classification performance.

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  - Xiaoyuan Jing
AU  - Xianglong Ge
AU  - Yongfang Yao
AU  - Fengnan Yu
PY  - 2013/03
DA  - 2013/03
TI  - FEATURE EXTRACTION ALGORITHM BASED ON SAMPLE SET RECONSTRUCTION
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2796
EP  - 2799
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.698
DO  - 10.2991/iccsee.2013.698
ID  - Jing2013/03
ER  -