@Article{jihpp.2020.010486,
AUTHOR = {Yan Zhou, Lili Pan, Rongyu Chen, Weizhi Shao},
TITLE = {A Novel Image Retrieval Method with Improved DCNN and Hash},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {77--86},
URL = {http://www.techscience.com/jihpp/v2n2/40537},
ISSN = {2637-4226},
ABSTRACT = {In large-scale image retrieval, deep features extracted by Convolutional 
Neural Network (CNN) can effectively express more image information than those 
extracted by traditional manual methods. However, the deep feature dimensions 
obtained by Deep Convolutional Neural Network (DCNN) are too high and 
redundant, which leads to low retrieval efficiency. We propose a novel image 
retrieval method, which combines deep features selection with improved DCNN 
and hash transform based on high-dimension features reduction to gain lowdimension deep features and realizes efficient image retrieval. Firstly, the 
improved network is based on the existing deep model to build a more profound 
and broader network by adding multiple groups of different branches. Therefore, 
it is named DFS-Net (Deep Feature Selection Network). The adaptive learning 
deep features of the Network can effectively alleviate the influence of over-fitting 
and improve the feature expression of image content. Secondly, the information 
gain rate method is used to filter the extracted deep features to reduce the feature 
dimension and ensure the information loss is small. The last step of the method, 
hash Transform, sparsifies and binarizes this representation to reduce the 
computation and storage pressure while maintaining the retrieval accuracy. Finally, 
the scheme is based on the distinguished ResNet50, InceptionV3, and 
MobileNetV2 models, and studied and evaluated deeply on the CIFAR10 and 
Caltech256 datasets. The experimental results show that the novel method can 
train the deep features with stronger recognition ability on limited training samples,
and improve the accuracy and efficiency of image retrieval effectively.},
DOI = {10.32604/jihpp.2020.010486}
}