scholarly journals An Encrypted Image Retrieval Method Based on SimHash in Cloud Computing

2020 ◽  
Vol 62 (3) ◽  
pp. 389-399
Author(s):  
Jiaohua Qin ◽  
Yusi Cao ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Lingyun Xiang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Image Retrieval ◽  
Retrieval Method ◽  
Encrypted Image

scholarly journals An Encrypted Image Retrieval Method Based on Harris Corner Optimization and LSH in Cloud Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 24626-24633 ◽  
Author(s):  
Jiaohua Qin ◽  
Hao Li ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Wenyan Pan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Image Retrieval ◽  
Retrieval Method ◽  
Encrypted Image ◽  
Harris Corner

scholarly journals Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Storage Space ◽  
Remote Sensing Images ◽  
Retrieval Method ◽  
Organization Management ◽  
Deep Feature ◽  
Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

Sign in / Sign up

Export Citation Format

Share Document