A Document Image Retrieval Method Based on Multi-feature Fusion

2016 ◽  
Author(s):  
Zhiyuan Zhu ◽  
Dongchun Ren ◽  
Guangyou Zhou ◽  
Yin Zhou
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Document Image ◽  
Retrieval Method

scholarly journals Hierarchical Image Retrieval by Multi-Feature Fusion

2017 ◽  
Author(s):  
Xiaojun Lu ◽  
Jiaojuan Wang ◽  
Yingqi Hou ◽  
Mei Yang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Time ◽  
Retrieval System ◽  
Feature Fusion ◽  
Retrieval Method ◽  
Time Consumption ◽  
Retrieval Accuracy ◽  
Single Feature ◽  
Image Retrieval System ◽  
Accuracy Rates

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

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.

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