Abstract
PCANet is a very simple learning network for image classification. Inspired by PCANet, we propose a new learning network, referred to as LPPNet, for image feature extraction and classification. Different from PCANet, LPPNet takes the class information and the local geometric structure of data into account simultaneously. In LPPNet, local preserving projections (LPP) is first employed to learn filters, and then binary hashing and block histograms are used for indexing and pooling. Experimental results on several image datasets verify the effectiveness and robustness of LPPNet for image feature extraction and classification.
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References
Jain, A.K., Duin, R.P.W., Miao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (2002)
Tenenbaum, J.B., Silva, V.d., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)
Lai, Z., Xu, Y., Yang, J., Shen, L., Zhang, D.: Rotational invariant dimensionality reduction algorithms. IEEE Trans. Cybern. 47(11), 3733–3746 (2017)
Shi, X., Guo, Z., Lai, Z., Yang, Y., Bao, Z., Zhang, D.: A framework of joint graph embedding and sparse regression for dimensionality reduction. IEEE Trans. Image Process. 24(4), 1341–1355 (2015)
Wang, Q., Gao, Q., Xie, D., Gao, X., Wang, Y.: Robust dlpp with nogreedy \(\ell_1\) minimization and maximization. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 738–743 (2018)
Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1615–1618 (2010)
Acknowledgments
This work is supported in part by the NSFC-Henan Talent Jointly Training Foundation of China (no. U1504621) and the Key Scientific Research Project of University in Henan Province of China (no. 18A120001).
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Li, G., Du, H., Xiao, M., Wang, S. (2018). LPPNet: A Learning Network for Image Feature Extraction and Classification. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_20
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DOI: https://doi.org/10.1007/978-3-319-97909-0_20
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