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Pcanet: A simple deep learning baseline for image classification?” arXiv preprint arXiv:1404.3606 (2014)
| Citations: | 10 - 0 self |
BibTeX
@MISC{Chan14pcanet:a,
author = {Tsung-han Chan and Kui Jia and Shenghua Gao and Jiwen Lu and Senior Member and Zinan Zeng and Yi Ma},
title = {Pcanet: A simple deep learning baseline for image classification?” arXiv preprint arXiv:1404.3606},
year = {2014}
}
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Abstract
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 com-ponent 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
