Ceramic image retrieval method based on HSH-SIFT fusion features

2021 ◽  
Author(s):  
Yongkang Peng ◽  
Jinjing Ma ◽  
Wenfang Cheng ◽  
Yu Nie
Keyword(s):  
Image Retrieval ◽  
Retrieval Method ◽  
Fusion Features

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.

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

2014 ◽  
Vol 40 (8) ◽  
pp. 154-162 ◽  
Author(s):  
Shailendrakumar M. Mukane ◽  
Sachin R. Gengaje ◽  
Dattatraya S. Bormane
Keyword(s):  
Fuzzy Logic ◽  
Image Retrieval ◽  
Texture Image ◽  
Rotation Invariant ◽  
Retrieval Method

A FACE IMAGE RETRIEVAL METHOD BASED ON ONTOLOGY

2021 ◽  
Author(s):  
Nguyen Van Thinh ◽  
Dang Van Thanh Nhan ◽  
Dinh Thi Man ◽  
Nguyen The Huu ◽  
Van The Thanh
Keyword(s):  
Image Retrieval ◽  
Face Image ◽  
Retrieval Method

scholarly journals Color-Based Image Retrieval Using Proximity Space Theory

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 115 ◽  
Author(s):  
Jing Wang ◽  
Lidong Wang ◽  
Xiaodong Liu ◽  
Yan Ren ◽  
Ye Yuan
Keyword(s):  
Image Retrieval ◽  
Image Similarity ◽  
Space Theory ◽  
Query Image ◽  
Proximity Space ◽  
Retrieval Method ◽  
Color Features ◽  
Granule Structure ◽  
Structure Similarity ◽  
Similarity Method

The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications.

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