UM
PCANet: A Simple Deep Learning Baseline for Image Classification?
Chan T.-H.; Jia K.; Gao S.; Lu J.; Zeng Z.; Ma Y.
2015
Source PublicationIEEE Transactions on Image Processing
ISSN10577149
Volume24Issue:12Pages:5017
AbstractIn 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 features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition. © 2015 IEEE.
KeywordConvolution Neural Network Deep Learning Face Recognition Handwritten Digit Recognition LDA Network Object Classification PCA Network Random Network
DOI10.1109/TIP.2015.2475625
URLView the original
Language英语
The Source to ArticleScopus
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Cited Times [WOS]:358   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
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GB/T 7714
Chan T.-H.,Jia K.,Gao S.,et al. PCANet: A Simple Deep Learning Baseline for Image Classification?[J]. IEEE Transactions on Image Processing,2015,24(12):5017.
APA Chan T.-H.,Jia K.,Gao S.,Lu J.,Zeng Z.,&Ma Y..(2015).PCANet: A Simple Deep Learning Baseline for Image Classification?.IEEE Transactions on Image Processing,24(12),5017.
MLA Chan T.-H.,et al."PCANet: A Simple Deep Learning Baseline for Image Classification?".IEEE Transactions on Image Processing 24.12(2015):5017.
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