A New Convolution Network Based on Laplacian Eigenmap

DOI

10.2991/icmmct-17.2017.153How to use a DOI?

Keywords

Convolution Network, Laplacian Eigenmap, texture classification, face recognition

Abstract

The best is to read these instructions and follow the outline of this text. Recently, convolutional deep neural network (ConvNet) has been widely used in the field of image classification. In this work, we propose a new feedback free convolution network for image classification. The proposed network could hierarchically and effectively extract the features from an image through a manually designed convolution network without relying on back-propagation. The network is designed in a cascaded fashion, where the Laplacian Eigenmap filter is used as convolution kernel to extract features in each of the cascaded stage. The final output of the network is achieved by a simply binary hashing and histogram encoding, and could be served as distinguishing features for many classification tasks. Experiments on different database, e.g. FERET datasets for face recognition, CUReT for texture classification and MNIST for hand-written digits recognition, showed that the proposed method outperforms many other popular machine learning algorithms.

Copyright

© 2017, 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  - Pengxin Kang
AU  - Xiaohai He
AU  - Lingbo Qing
AU  - Qizhi Teng
AU  - Jie Su
PY  - 2017/04
DA  - 2017/04
TI  - A New Convolution Network Based on Laplacian Eigenmap
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 759
EP  - 765
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.153
DO  - 10.2991/icmmct-17.2017.153
ID  - Kang2017/04
ER  -