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.

scholarly journals Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model

2009 ◽  
Vol 2009 ◽  
pp. 1-17 ◽  
Author(s):  
Meng-Hsiun Tsai ◽  
Yung-Kuan Chan ◽  
Jiun-Shiang Wang ◽  
Shu-Wei Guo ◽  
Jiunn-Lin Wu
Keyword(s):  
Image Retrieval ◽  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Query Image ◽  
Retrieval Method ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Gaussian Markov Random Field ◽  
Markov Random

The techniques of -means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations.

Research on Industry Image Technology with the Integration Algorithm of Color Features and Feedback of Image Retrieval

2014 ◽  
Vol 1014 ◽  
pp. 520-524 ◽  
Author(s):  
Shui Li Zhang
Keyword(s):  
Image Retrieval ◽  
Feedback Mechanism ◽  
Image Features ◽  
Feature Integration ◽  
Retrieval Performance ◽  
Integration Algorithm ◽  
Retrieval Method ◽  
Current Image ◽  
Color Features ◽  
The Individual

Current image retrieval methods use only one kind of image features, which can not describe image content completely. In this paper, an image retrieval method based on color and texture integration is proposed. Calculation of similarity for the individual features is normalized before color feature integration. In order to improve the precision of image retrieval, a relevance feedback mechanism is invoked. The experiment results show that the proposed method has an excellent retrieval performance.

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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