Using ant colony optimization and self-organizing map for image segmentation

被引：0
|
作者：
Saatchi, Sara [1 ]
Hung, Chih-Cheng [1 ]
机构：
[1] Southern Polytech State Univ, Sch Comp & Software Engn, 1100 S Marietta Pkwy, Marietta, GA 30060 USA
来源：
MICAI 2007: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2007年 / 4827卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, ant colony optimization (ACO) is integrated with the self-organizing map (SOM) for image segmentation. A comparative study with the combination of ACO and Simple Competitive Learning (SCL) is provided. ACO follows a learning mechanism through pheromone updates. In addition, pheromone and heuristic information are normalized and the effects on the results are investigated in this report. Preliminary experimental results indicate that the normalization of the parameters can improve the image segmentation results.
引用
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页码：570 / +
页数：2
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