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Color-Based Image Retrieval Using Proximity Space Theory

College of Science, Dalian Maritime University, Dalian 116026, China
School of Automation, Shenyang Aerospace University, Shenyang 110136, China
Authors to whom correspondence should be addressed.
Algorithms 2018, 11(8), 115;
Received: 30 June 2018 / Revised: 22 July 2018 / Accepted: 26 July 2018 / Published: 28 July 2018
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. View Full-Text
Keywords: color features; proximity space; image retrieval; similarity measurement color features; proximity space; image retrieval; similarity measurement
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MDPI and ACS Style

Wang, J.; Wang, L.; Liu, X.; Ren, Y.; Yuan, Y. Color-Based Image Retrieval Using Proximity Space Theory. Algorithms 2018, 11, 115.

AMA Style

Wang J, Wang L, Liu X, Ren Y, Yuan Y. Color-Based Image Retrieval Using Proximity Space Theory. Algorithms. 2018; 11(8):115.

Chicago/Turabian Style

Wang, Jing, Lidong Wang, Xiaodong Liu, Yan Ren, and Ye Yuan. 2018. "Color-Based Image Retrieval Using Proximity Space Theory" Algorithms 11, no. 8: 115.

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