Feature Extraction Algorithm Based on 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.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 2241-2245
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.2241
• Online since: August 2013
• Authors: Xiao Yuan Jing, Xiang Long Ge, Yong Fang Yao, Feng Nan Yu
• Keywords: Mage Recognition, Sample Set Reconstruction, Transfer Learning

References

[1] H. Yu, J. Yang, A direct LDA algorithm for high-dimensional data-with application to face recognition, Pattern Recognition, pp: 2067-2070, (2001).

DOI: 10.1016/s0031-3203(00)00162-x

[2] P. N. Belhumeur, J. Hespanda and D. Kiregeman, Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection, , IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp.711-720, (1997).

DOI: 10.1109/34.598228

[3] Sinno Jialin Pan and Qiang Yang, A survey on Transfer Learning, IEEE Trans. Knowledge and Data Engineering, 2010, 22(10).

[4] D. Silver and R. Mercer, Selective Functional Transfer: Inductive Bias From Related Tasks, In Proc. IASTED Int. Conf. Artif. Intell. Soft Comput, 2001, 182-189.

[5] J. Schmidhuber, On learning how to learn learning strategies, Technical Report FKI-198-94, Fakultat fur Informatik, (1994).

[6] Y. Freund and R. E. Schapire, A Decision-theoretic Generalization of On-line Learning and an Apcation to Boosting, Journal of Computer and System Sciences, 1997, 55(1): 119-139.

DOI: 10.1006/jcss.1997.1504

[7] Sinno Jialin Pan, James T. Kwok and Qiang Yang, Transfer Learning via Dimensionality Reduction, In Proceedings of AAAI, 2008, 677-682.

[8] Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok and Qiang Yang. Domain Adaptation via Transfer Component Analysis, IEEE Trans. Neural Networks, 2011, 22(2): 199-210.

DOI: 10.1109/tnn.2010.2091281

[9] Martinez, A. M., and Kak, A. C, PCA versus LDA, IEEE Trans. Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.

DOI: 10.1109/34.908974

[10] P. J. Phillips, H. Wechsler, J. Huang and P. Rauss, The FERET Database and Evaluation Procedure for Face-Recognition Algorithms,. Image and Vision Computing. 1998, 16: 295-306.

DOI: 10.1016/s0262-8856(97)00070-x

[11] W. Gao , B. Cao, S. Shan, X. Chen, D. Zhou, X. Zhang, D. Zhao. The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations, IEEE Trans. System Man, and Cybernetics (Part A), 2008, 38(1): 149-161.

DOI: 10.1109/tsmca.2007.909557