Application of Optimized Partitioning Around Medoid Algorithm in Image Retrieval

Yanxia Jin, Xin Zhang and Yao Jia
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Yanxia Jin: North University of China, China
Xin Zhang: Beihang University, China
Yao Jia: North University of China, China

International Journal of Distributed Systems and Technologies (IJDST), 2021, vol. 12, issue 1, 77-94

Abstract: In image retrieval, the major challenge is that the number of images in the gallery is large and irregular, which results in low retrieval accuracy. This paper analyzes the disadvantages of the PAM (partitioning around medoid) clustering algorithm in image data classification and the excessive consumption of time in the computation process of searching clustering representative objects using the PAM clustering algorithm. Fireworks particle swarm algorithm is utilized in the optimization process. PF-PAM algorithm, which is an improved PAM algorithm, is proposed and applied in image retrieval. First, extract the feature vector of the image in the gallery for the first clustering. Next, according to the clustering results, the most optimal cluster center is searched through the firework particle swarm algorithm to obtain the final clustering result. Finally, according to the incoming query image, determine the related image category and get similar images. The experimental comparison with other approaches shows that this method can effectively improve retrieval accuracy.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdst00:v:12:y:2021:i:1:p:77-94

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