PCANet: A Simple Deep Learning Baseline for Image Classification?

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Tsung-Han ChanYi Ma

Abstract

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art feature...Continue Reading

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Citations

Oct 1, 2016·BioMed Research International·Bineng ZhongLiujuan Cao
Oct 14, 2016·Environmental Science and Pollution Research International·Xiang LiTianhe Chi
Jan 20, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yang WangWenjie Zhang
Apr 15, 2017·IEEE Journal of Biomedical and Health Informatics·Yue HuangGustavo Rohde
May 15, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Homa Foroughi Hong Zhang
Jul 17, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Xiaojie Guo, Zhouchen Lin
Sep 9, 2017·IEEE Transactions on Cybernetics·Guanqun CaoMoncef Gabbouj
Aug 10, 2019·Computational Intelligence and Neuroscience·Yuliang MaYingchun Zhang
Jun 5, 2020·Cognitive Science·Robert D HawkinsNoah D Goodman
May 4, 2017·Evidence-based Complementary and Alternative Medicine : ECAM·Dan MengJiatuo Xu
Dec 15, 2019·Journal of Biomedical Optics·Lopamudra MukherjeeKevin Eliceiri
Oct 31, 2018·Sensors·Cristhian A AguileraAngel D Sappa
Mar 12, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Zebin YangAijun Zhang
Mar 9, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Zhao ZhangMeng Wang
Jul 14, 2020·Computers in Biology and Medicine·Shenda HongJimeng Sun
Mar 19, 2021·Environmental Pollution·Pei-Yi WongChih-Da Wu

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