Support vector machine image recognition based on particle swarm

Xiao-yan HAN; Dong ZHAO

doi:10.3788/YJYXS20173201.0069

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Support vector machine image recognition based on particle swarm

Display and Image | 更新时间：2020-07-07

- Support vector machine image recognition based on particle swarm
- Chinese Journal of Liquid Crystals and Displays Vol. 32, Issue 1, Pages: 69-75(2017)
- 作者机构：
  
  1.吉林农业大学信息技术学院, 吉林长春 130118
  2.长春理工大学光电信息学院信息工程分院, 吉林长春 130012
  3.长春师范大学计算机科学与技术学院, 吉林长春 130032
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20173201.0069
  CLC： TP394.1;TH691.9
- Received：11 August 2016，
  
  Accepted：17 October 2016，
  
  Published：2017-01
- 稿件说明：
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Xiao-yan HAN, Dong ZHAO. Support vector machine image recognition based on particle swarm[J]. Chinese journal of liquid crystals and displays, 2017, 32(1): 69-75.
DOI：

Xiao-yan HAN, Dong ZHAO. Support vector machine image recognition based on particle swarm[J]. Chinese journal of liquid crystals and displays, 2017, 32(1): 69-75. DOI： 10.3788/YJYXS20173201.0069.

摘要

为了实现对田间水稻缺素的精准识别，构建一个图像识别系统。对该系统所采用的图像采集、图像分割、基于支持向量机图像分类等算法进行研究。首先，根据田间水稻的缺素现象进行图像采集和处理。然后提取图像与氮元素相关的颜色特征。在分析比较SVM算法对图像分割的基础上，提出一种基于改进粒子群算法进行SVM参数优化算法模型（即IPSO-SVM）。最后，对实验进行设置，对算法模型与其他算法进行测试对比。实验结果表明：对水稻缺素诊断的准确率达到95.45%，基本满足田间水稻缺素的科学诊断要求。

Abstract

In order to realize the precise identification of the missing elements in the field of rice

an image recognition system was constructed. The image acquisition

image segmentation and image classification based on support vector machine are studied in this system. Firstly

image acquisition and processing were carried out according to the phenomenon of deficiency of rice in the field. Then the color features were extracted related to the image of nitrogen elements. On the basis of analyzing and comparing the SVM algorithm to image segmentation

an improved particle swarm optimization algorithm is proposed to optimize the SVM parameters (i.e. IPSO-SVM). At last

the experiment is set up

and the algorithm model is tested and compared with other algorithms. The experimental results show that the accuracy rate of the diagnosis of rice deficiency is 95.45%

which basically meets the requirements of the scientific diagnosis of the deficiency of rice in the field.

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Keywords

references

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