Open Access
ARTICLE
A Novel Image Retrieval Method with Improved DCNN and Hash
Yan Zhou, Lili Pan*, Rongyu Chen, Weizhi Shao
Central South University of Forestry and Technology, Changsha, 410000, China
* Corresponding Author: Lili Pan. Email:
Journal of Information Hiding and Privacy Protection 2020, 2(2), 77-86. https://doi.org/10.32604/jihpp.2020.010486
Received 25 May 2020; Accepted 10 July 2020; Issue published 11 November 2020
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.
Keywords
Cite This Article
Y. Zhou, L. Pan, R. Chen and W. Shao, "A novel image retrieval method with improved dcnn and hash,"
Journal of Information Hiding and Privacy Protection, vol. 2, no.2, pp. 77–86, 2020. https://doi.org/10.32604/jihpp.2020.010486