【疾病分类】基于matlab SVM农作物叶子虫害识别与分类【含Matlab源码 624期】

一、SVM简介

支持向量机(Support Vector Machine)是Cortes和Vapnik于1995年首先提出的，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。
1 数学部分
1.1 二维空间









2 算法部分

二、部分源代码

clear all
clc

disp('正在训练农作物叶子图像模板，请稍后...');
disp(' ');
%color_Ip = xunlian();
pause(2);
load C:\Users\lenovo\Desktop\图像检索\color_Ip.mat;
disp('图像训练完成，正在进行图像识别，请稍后...');
disp(' ');
pause(2);

path = input('请输入待识别叶子图像路径：'); % 'F:\病虫害识别\图像检索\示例2中等.jpg'
A = imread(path); % 读入叶子图像
G0 = lianghua_hsv(A); % 量化hsv分量并获得颜色直方图
color_Iq = color_feature(G0); % 提取颜色特征
color_Dpq = color_match(color_Ip,color_Iq); % 颜色特征匹配

[r c] = find(min(min(color_Dpq))==color_Dpq);
A=imread('F:\病虫害识别\图像检索\1.jpg');



[M,N,O] = size(A);
[h,s,v] = rgb2hsv(A);

H = h; S = s; V = v;
h = h*360; 
%将hsv空间非等间隔量化：
%  h量化成16级；
%  s量化成4级；
%  v量化成4级；
for i = 1:M
    for j = 1:N
        if h(i,j)<=15||h(i,j)>345
            H(i,j) = 0;
        end
        if h(i,j)<=25&&h(i,j)>15
            H(i,j) = 1;
        end
        if h(i,j)<=45&&h(i,j)>25
            H(i,j) = 2;
        end
        if h(i,j)<=55&&h(i,j)>45
            H(i,j) = 3;
        end
        if h(i,j)<=80&&h(i,j)>55
            H(i,j) = 4;
        end
        if h(i,j)<=108&&h(i,j)>80
            H(i,j) = 5;
        end
        if h(i,j)<=140&&h(i,j)>108
            H(i,j) = 6;
        end
        if h(i,j)<=165&&h(i,j)>140
            H(i,j) = 7;
        end
        if h(i,j)<=190&&h(i,j)>165
            H(i,j) = 8;
        end
        if h(i,j)<=220&&h(i,j)>190
            H(i,j) = 9;
        end
        if h(i,j)<=255&&h(i,j)>220
            H(i,j) = 10;
        end
        if h(i,j)<=275&&h(i,j)>255
            H(i,j) = 11;
        end
        if h(i,j)<=290&&h(i,j)>275
            H(i,j) = 12;
        end
        if h(i,j)<=316&&h(i,j)>290
            H(i,j) = 13;
        end
        if h(i,j)<=330&&h(i,j)>316
            H(i,j) = 14;
        end
        if h(i,j)<=345&&h(i,j)>330
            H(i,j) = 15;
        end
    end
end
for i = 1:M
    for j = 1:N
        if s(i,j)<=0.15&&s(i,j)>0
            S(i,j) = 1;
        end
        if s(i,j)<=0.4&&s(i,j)>0.15
            S(i,j) = 2;
        end
        if s(i,j)<=0.75&&s(i,j)>0.4
            S(i,j) = 3;
        end
        if s(i,j)<=1&&s(i,j)>0.75
            S(i,j) = 4;
        end
    end
end
for i = 1:M
    for j = 1:N
        if v(i,j)<=0.15&&v(i,j)>0
            V(i,j) = 1;
        end
        if v(i,j)<=0.4&&v(i,j)>0.15
            V(i,j) = 2;
        end
        if v(i,j)<=0.75&&v(i,j)>0.4
            V(i,j) = 3;
        end
        if v(i,j)<=1&&v(i,j)>0.75
            V(i,j) = 4;
        end
    end
end


% 构建4*16二维数组存放H-S数据
Hist = zeros(16,4);
for i = 1:M
    for j = 1:N
        for k = 1:16
            for l = 1:4
                if  l==S(i,j)&& k==H(i,j)+1
                    Hist(k,l) = Hist(k,l)+1;
                end
            end
        end
    end
end
for k = 1:16
    for l =1:4
        His((k-1)*4+l) = Hist(k,l);%转化为一维数组
    end
end
His = His/sum(His)*1000;
% 手工绘制彩色图像直方图
% hist_h
m=0;
for j = 1:300
    if rem(j,16)==1 && m<16
        for k = 0:15
            for i = 1:200
                hist_h(i,j+k) = m;
            end           
        end
        m = m+1;
    end
end
% hist_s
m=0;
for j = 1:300
    if rem(j,4) == 1 && m<64
        n = rem(m,4);
        for k = 0:3             
            for i =1:200             
                hist_s(i,j+k) = n+1;               
            end                    
        end
        m = m+1;
    end   
end
% hist_v
for j = 1:256
    for i = 1:200
        hist_v(i,j) = 0.98;
    end
end
% 把His赋值给hist_v
for k = 1:64
    for j = 1:256
        if floor((j-1)/4) == k
            for i = 1:200
                if i<200-His(k+1)%i>His(k+1)%
                    hist_v(i,j) = 0;
                end
            end
        end
    end
end

  

 

  
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三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.

文章来源: qq912100926.blog.csdn.net，作者：海神之光，版权归原作者所有，如需转载，请联系作者。

原文链接：qq912100926.blog.csdn.net/article/details/115190641