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LPPNet: A Learning Network for Image Feature Extraction and Classification

  • Guodong Li
  • Haishun Du
  • Meihong Xiao
  • Sheng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

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.

Keywords

LPPNet Learning network Feature extraction Image classification 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guodong Li
    • 1
  • Haishun Du
    • 1
  • Meihong Xiao
    • 1
  • Sheng Wang
    • 1
  1. 1.School of Computer Science and Information EngineeringHenan UniversityKaifengChina

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