Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm

CHEN Kai; CHEN Fang; DAI Min; ZHANG Zhi-sheng; SHI Jin-fei

doi:10.3788/OPE.20142202.0517

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Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm

Information Sciences | 更新时间：2020-08-13

- Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm
- Optics and Precision Engineering Vol. 22, Issue 2, Pages: 517-523(2014)
- 作者机构：
  
  1. 东南大学机械工程学院,江苏南京,211189
  2. 淮海工学院,江苏连云港,222005
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142202.0517
  CLC： TP391.4
- Received：10 May 2013，
  
  Published：20 February 2014
- 稿件说明：
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陈恺,陈芳,戴敏等. 基于萤火虫算法的二维熵多阈值快速图像分割[J]. 光学精密工程, 2014,22(2): 517-523

CHEN Kai, CHEN Fang, DAI Min etc. Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm[J]. Editorial Office of Optics and Precision Engineering, 2014,22(2): 517-523
陈恺,陈芳,戴敏等. 基于萤火虫算法的二维熵多阈值快速图像分割[J]. 光学精密工程, 2014,22(2): 517-523 DOI： 10.3788/OPE.20142202.0517.

CHEN Kai, CHEN Fang, DAI Min etc. Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm[J]. Editorial Office of Optics and Precision Engineering, 2014,22(2): 517-523 DOI： 10.3788/OPE.20142202.0517.

摘要

提出了基于萤火虫算法的二维熵多阈值快速图像分割方法以改善分割复杂图像和多目标图像时存在计算量大、计算时间长的问题。首先

分析了二维熵阈值分割原理

将二维熵单阈值分割扩展到二维熵多阈值分割。然后

引入萤火虫算法的思想

研究了萤火虫算法的仿生原理和寻优过程;提出了基于萤火虫算法的二维熵多阈值快速图像分割方法。最后

使用该方法对典型图像进行阈值分割实验

并与二维熵穷举分割法、粒子群算法(PSO)二维熵多阈值分割法进行比较。实验结果表明:该方法在单阈值分割、双阈值分割和三阈值分割时分别比二维熵穷举分割法快3.91倍

1040.32倍和8128.85倍;另外

在阈值选取的准确性和计算时间方面均优于PSO二维熵多阈值分割法。结果显示

基于萤火虫算法的二维熵多阈值快速图像分割方法能快速有效地解决复杂图像和多目标图像的分割问题。

Abstract

A fast image segmentation method with multilevel threshold of two-dimensional entropy was proposed based on the firefly algorithm to overcome the large amount of calculation and long computing time. Firstly

the principle of two-dimensional entropy threshold segmentation was analyzed

and the single threshold segmentation of two-dimensional entropy was extended to multilevel threshold segmentation. Then

the bionic mechanism and searching optimization process of the firefly algorithm were analyzed

and the multilevel threshold segmentation method of two-dimensional entropy combined with firefly algorithm was proposed. Finally

typical image segmentation experiments by using the proposed method were performed and the results were compared with those of two-dimensional entropy exhaustive segmentation method and the multilevel threshold segmentation method of two-dimensional entropy based on Particle Swarm Optimization(PSO). Experimental results show that the speeds of the proposed method in single threshold segmentation

dual-threshold segmentation and the three threshold segmentation are 3.91

1 040.32 and 8 128.85 times faster than those of the two-dimensional entropy exhaustive segmentation method respectively. Moreover

the threshold selection accuracy and running speed of the proposed method are both better than those of the multilevel threshold segmentation method of two-dimensional entropy based on PSO. Therefore

the fast image segmentation method with multilevel threshold of two-dimensional entropy based on firefly algorithm can quickly and efficiently resolve complex and multi-target image segmentation problems.

关键词

Keywords

references

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Related Institution

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College of Information， Chang'an University

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