FEATURE EXTRACTION ALGORITHM BASED ON SAMPLE SET RECONSTRUCTION
- 10.2991/iccsee.2013.698How to use a DOI?
- image recognition, transfer learning, sample set reconstruction
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.
- © 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/).
Cite this article
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 -