A Memetic Particle Gravitation Optimization Algorithm for Solving Image Segmentation

被引：0

作者：

Huang, Ko-Wei ^{[1
]}

Wu, Ze-Xue ^{[1
]}

Peng, Hsing-Wei ^{[1
]}

Tsai, Ming-Chia ^{[1
]}

Hung, Yu-Chieh ^{[1
]}

Lu, Yu-Chin ^{[1
]}

机构：

[1] Natl Kaohsiung Univ Sci & Technol, Dept Elect Engn, Kaohsiung, Taiwan

来源：

PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ) | 2018年

关键词：

Clustering; Image Segmentation; Particle Swarm Optimization; Gravitation Search Algorithm; Memetic Algorithm;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Significant objects can be obtained from the gray or color images via image segmentation by referring to the clustering problem. Inspired by the search strategy of GSA and the rapid convergence of PSO, this study proposes a memetic particle gravitation optimization (MPGO) algorithm, which is a hybrid approach for performing image segmentation. The proposed MPGO algorithm is verified using six well-known images, including Lena, Baboon, F16, Goldhill, Pepper, and Sailboat. MPGO significantly outperforms k-means, PSO and GSA in measures of the PSNR values.

引用

页码：82 / 85

页数：4

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