基于粒子群的支持向量机图像识别

韩晓艳; 赵东

doi:10.3788/YJYXS20173201.0069

您当前的位置：

首页 >

文章列表页 >

基于粒子群的支持向量机图像识别

显示与图像 | 更新时间：2020-07-07

- 基于粒子群的支持向量机图像识别
- Support vector machine image recognition based on particle swarm
- 液晶与显示 2017年32卷第1期页码：69-75
- 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：
  
  [ "韩晓艳(1978-),女,吉林德惠人,硕士研究生,主要从事数据挖掘与云计算方面的研究。E-mail:37754961@qq.com" ]
  赵东(1978-),男,吉林德惠人,副教授,博士研究生,主要从事智能信息处理与嵌入式技术的研究。E-mail:zd-hy@163.com
- 基金信息：
  
  国家自然科学基金(No.61101155);吉林省自然科学基金(No.20140101184JC);长春市科技计划资助项目(No.2012091);吉发改高技(No.2014817);吉林省教育厅(No.2016392);Supported by National Natural Science Foundation of China(No.61101155);Jilin Province Natural Science Foundation of China(No.20140101184JC);Changchun Science and Technology Plan Projects(No.2012091);Jilin Province Development and Reform Commission High Technology Industry Development(No.2014817);Jilin province Education Department(No.2016392)
- DOI：10.3788/YJYXS20173201.0069
  中图分类号： TP394.1;TH691.9
- 收稿日期：2016-08-11，
  
  录用日期：2016-10-17，
  
  纸质出版日期：2017-01
- 稿件说明：
移动端阅览
韩晓艳, 赵东. 基于粒子群的支持向量机图像识别[J]. 液晶与显示, 2017,32(1):69-75.

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.
韩晓艳, 赵东. 基于粒子群的支持向量机图像识别[J]. 液晶与显示, 2017,32(1):69-75. DOI： 10.3788/YJYXS20173201.0069.

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.

关键词

Keywords

references

刘伟,王建平,刘长虹,等. 基于粒子群寻优的支持向量机番茄红素含量预测[J]. 农业机械学报, 2012,43(4):143-147,155.

LIU W, WANG J P, LIU C H,et al. Lycopene content prediction based on support vector machine with particle swarm optimization[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(4):143-147, 155. (in Chinese)

李冠林,马占鸿,王海光. 基于支持向量机的小麦条锈病和叶锈病图像识别[J]. 中国农业大学学报, 2012,17(2):72-79.

LI G L, MA Z H, WANG H G. Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine[J]. Journal of China Agricultural University, 2012, 17(2):72-79. (in Chinese)

田有文,王立地,姜淑华. 基于图像处理和支持向量机的玉米病害识别[J]. 仪器仪表学报, 2006,27(S6):2123-2124.

TIAN Y W, TIAN L D, JIANG S H. Recognition of maize disease based on image processing and SVM[J]. Chinese Journal of Scientific Instrument, 2006, 27(S6):2123-2124. (in Chinese)

郭敬明,何昕,魏仲慧. 基于在线支持向量机的Mean Shift彩色图像跟踪[J]. 液晶与显示, 2014,29(1):120-128.

GUO J M, HE X, WEI Z H. New mean shift tracking for color image based on online support vector machine[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(1):120-128. (in Chinese)

岳有军,杨雪,赵辉,等. 基于支持向量机的油菜缺素诊断研究[J]. 广东农业科学, 2015,42(20):145-148.

YUE Y J, YANG X, ZHAO H,et al. Diagnosis of rape nutrient deficiency based on support vector machine[J]. Guangdong Agricultural Sciences, 2015, 42(20):145-148. (in Chinese)

OMRAN M, ENGELBRECHT A P, SALMAN A. Particle swarm optimization method for image clustering[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2005, 19(3):297-321.

汪海燕,黎建辉,杨风雷. 支持向量机理论及算法研究综述[J]. 计算机应用研究, 2014,31(5):1281-1286.

WANG H Y, LI J H, YANG F L. Overview of support vector machine analysis and algorithm[J]. Application Research of Computers, 2014, 31(5):1281-1286. (in Chinese)

CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.

JIAO L C, BO L F, WANG L. Fast sparse approximation for least squares support vector machine[J]. IEEE Transactions on Neural Networks, 2007, 18(3):685-697.

刘淳安. 一种求解动态多目标优化问题的粒子群算法[J]. 系统仿真学报, 2011,23(2):288-293.

LIU C A. Particle swarm algorithm for solving dynamic multi-objective optimization problems[J]. Journal of System Simulation, 2011, 23(2):288-293. (in Chinese)

程万胜,臧希喆,赵杰,等. 面向Otsu阈值搜索的PSO惯性因子改进方法[J]. 光学精密工程, 2008,16(10):1907-1912.

CHENG W S, ZANG X Z, ZHAO J,et al. Modified strategy to inertia weight in PSO for searching threshold of Otsu rule[J]. Optics and Precision Engineering, 2008, 16(10):1907-1912. (in Chinese)

崔长彩,黄富贵,张认成,等. 粒子群优化算法及其在圆柱度误差评定中的应用[J]. 光学精密工程, 2006,14(2):256-260.

CUI C B, HUANG F G, ZHANG R C,et al. Research on cylindricity evaluation based on the Particle Swarm Optimization(PSO)[J]. Optics and Precision Engineering, 2008, 14(2):256-260. (in Chinese)

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于边缘增强和特征融合的伪装目标分割

基于图像分割的点云去噪方法

基于NODE-UNet++和标记分水岭算法的红细胞图像分割

自适应动态肤色检测技术研究进展

基于Faster R-CNN的仪表识别方法