PCANet: A Simple Deep Learning Baseline for Image Classification?
PCANet: A Simple Deep Learning Baseline for Image Classification?
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DOI：
10.1109/tip.2015.2475625
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发表时间：
2015-12-01
影响因子：
10.6
通讯作者：
Ma, Yi
中科院分区：
文献类型：
--
作者：
Chan, Tsung-Han;Jia, Kui;Ma, Yi
在本文中，我们提出了一个非常简单的深度学习网络用于图像分类，它基于非常基本的数据处理组件：1)级联主成分分析(PCA)；2)二进制哈希；3)块直方图。在所提出的结构中，使用主成分分析来学习多级滤波器组。然后是简单的二进制散列和块直方图，用于索引和池化。这种体系结构因此被称为PCA网络(PCANet)，可以非常容易、高效地设计和学习。为了比较和更好地理解，我们还介绍和研究了PCANet的两个简单变体：1)RandNet和2)LDANet。它们与PCANet具有相同的拓扑结构，但它们的级联滤波器要么是随机选择的，要么是从线性判别分析中学习的。我们已经在不同任务的许多基准视觉数据集上广泛测试了这些基本网络，包括用于人脸验证的标签人脸(LFW)；用于人脸识别的MultiPIE、扩展耶鲁B、AR、面部识别技术(FERET)数据集；以及用于手写数字识别的MNIST。令人惊讶的是，对于所有任务，这样一个看似幼稚的PCANet模型与最先进的功能不相上下，要么是前缀的，要么是高度手工制作的，要么是[通过深度神经网络(DNN)]仔细学习的。更令人惊讶的是，该模型在扩展的耶鲁B、AR和FERET数据集以及MNIST变体上为许多分类任务创造了新的记录。在其他公共数据集上的其他实验也证明了PCANet作为纹理分类和目标识别的简单但极具竞争力的基线的潜力。
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 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.