A Feature-Based Document Image Retrieval Method

2008 ◽  
Author(s):  
Tian Zhang
Keyword(s):  
Image Retrieval ◽  
Document Image ◽  
Retrieval Method ◽  
Feature Based

A Shape-Based Two-Stage Product Image Retrieval Method

2012 ◽  
Vol 466-467 ◽  
pp. 1050-1054
Author(s):  
Shang Fu Gong ◽  
Juan Du
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Two Stage ◽  
Retrieval Method ◽  
Second Stage ◽  
Product Image ◽  
Feature Based ◽  
Large Product ◽  
Product Image Retrieval ◽  
Salient Edges

Product image retrieval using content of the image is valuable for E-commerce application. But both search efficiency and accuracy are challenging the implementation of content-based image retrieval in large product image database. We present a two-stage product image retrieval method, with fully consideration of individual features of product images. In the initial pruning stage, shape feature based on salient edges of product object is used to generate a moderate number of candidates; in the second stage, the proposed detail feature combined with color and texture features is used for fully retrieval. Experiments show that this two-stage retrieval method accelerates search process with a high 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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