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Dimension reduction of image deep feature using PCA

机译：使用PCA缩小图像深度特征的尺寸

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摘要

Convolution neural networks based methods can derive deep features from training images. However, one challenge is that the dimension of the extracted image features increases dramatically with more network layers. To solve this problem, this paper focuses on the study of dimension reduction. After using deep learning to extract image features, the PCA algorithm is used to achieve dimension reduction. Specifically, we first leverage deep convolutional neural network to extract image features. Then, we introduce and leverage PCA algorithm to achieve dimension reduction. Aiming at the problem that it is difficult to process high-dimensional sparse big data based on PCA algorithm. This paper optimizes the PCA algorithm. After image preprocessing, the feasibility of PCA algorithm for dimension reduction of image feature extraction by deep learning is verified by simulation experiments. The efficiency of the proposed algorithm is proved by comparing the performance of PCA algorithm before and after optimization. (C) 2019 Published by Elsevier Inc.
机译：基于卷积神经网络的方法可以从训练图像中得出深度特征。然而，一个挑战是，随着更多的网络层，提取的图像特征的尺寸会急剧增加。为了解决这个问题，本文着重研究降维。在使用深度学习提取图像特征之后，使用PCA算法实现降维。具体来说，我们首先利用深度卷积神经网络提取图像特征。然后，我们引入并利用PCA算法来实现降维。针对PCA算法难以处理高维稀疏大数据的问题。本文优化了PCA算法。经过图像预处理，通过仿真实验验证了PCA算法在深度学习中减少图像特征提取维数的可行性。通过比较优化前后PCA算法的性能，证明了该算法的有效性。 （C）2019由Elsevier Inc.发布

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