@Article{jihpp.2020.010472,
AUTHOR = {Rongyu Chen, Lili Pan, Yan Zhou, Qianhui Lei},
TITLE = {Image Retrieval Based on Deep Feature Extraction and Reduction with  Improved CNN and PCA},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {67--76},
URL = {http://www.techscience.com/jihpp/v2n2/40536},
ISSN = {2637-4226},
ABSTRACT = { With the rapid development of information technology, the speed and 
efficiency of image retrieval are increasingly required in many fields, and a 
compelling image retrieval method is critical for the development of information. 
Feature extraction based on deep learning has become dominant in image retrieval 
due to their discrimination more complete, information more complementary and 
higher precision. However, the high-dimension deep features extracted by CNNs 
(convolutional neural networks) limits the retrieval efficiency and makes it difficult 
to satisfy the requirements of existing image retrieval. To solving this problem, the 
high-dimension feature reduction technology is proposed with improved CNN and 
PCA quadratic dimensionality reduction. Firstly, in the last layer of the classical 
networks, this study makes a well-designed DR-Module (dimensionality reduction 
module) to compress the number of channels of the feature map as much as 
possible, and ensures the amount of information. Secondly, the deep features are 
compressed again with PCA (Principal Components Analysis), and the 
compression ratios of the two dimensionality reductions are reduced, respectively. 
Therefore, the retrieval efficiency is dramatically improved. Finally, it is proved on 
the Cifar100 and Caltech101 datasets that the novel method not only improves the 
retrieval accuracy but also enhances the retrieval efficiency. Experimental results 
strongly demonstrate that the proposed method performs well in small and 
medium-sized datasets.},
DOI = {10.32604/jihpp.2020.010472}
}