期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 63, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2019.102578
关键词
Deep learning; Feature extraction; Dimension reduction; PCA algorithm
资金
- National Natural Science Foundation of China [91118002]
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.
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