matlab如何svm实现图像分类输出像素点

matlab如何svm实现图像分类输出像素点，这里分享下 *** 作方法。

设备：华硕笔记本

系统：win10

软件：matlab2012

1、首先双击桌面matlab图标，打开matlab软件。

2、然后在打开的软件中，点击左上角新建m文件。

3、然后输入如图所示的代码。

4、最后点击运行程序，即可输出两幅图像像素点。

SVM在matlab中实现：

首先需要MATLAB SVM Toolbox，将其中的文件解压并命名为svm。

将文件拷到E:\matlab\toolbox。

打开matlab点击set path---->add folder ，然后把工具箱文件夹添加进去就可以了。

路径加进去后在file→Preferences→General的Toolbox Path Caching里点击update Toolbox Path Cache更新一下。

最后在matlab的命令栏中输入which svcoutput可以查看路径E:\matlab\toolbox\svm\svcoutput.m就可以了。

这个是非线性svm的：

1.命令函数部分：

clear%清屏

clc

X =load('data.txt')

n = length(X)%总样本数量

y = X(:,4)%类别标志

X = X(:,1:3)

TOL = 0.0001%精度要求

C = 1%参数，对损失函数的权重

b = 0%初始设置截距b

Wold = 0%未更新a时的W(a)

Wnew = 0%更新a后的W(a)

for i = 1 : 50%设置类别标志为1或者-1

y(i) = -1

end

a = zeros(n,1)%参数a

for i = 1 : n%随机初始化a,a属于[0,C]

a(i) = 0.2

end

%为简化计算，减少重复计算进行的计算

K = ones(n,n)

for i = 1 :n%求出K矩阵，便于之后的计算

for j = 1 : n

K(i,j) = k(X(i,:),X(j,:))

end

end

sum = zeros(n,1)%中间变量，便于之后的计算，sum(k)=sigma a(i)*y(i)*K(k,i)

for k = 1 : n

for i = 1 : n

sum(k) = sum(k) + a(i) * y(i) * K(i,k)

end

end

while 1%迭代过程

%启发式选点

n1 = 1%初始化，n1,n2代表选择的2个点

n2 = 2

%n1按照第一个违反KKT条件的点选择

while n1 <= n

if y(n1) * (sum(n1) + b) == 1 &&a(n1) >= C &&a(n1) <= 0

break

end

if y(n1) * (sum(n1) + b) >1 &&a(n1) ~= 0

break

end

if y(n1) * (sum(n1) + b) <1 &&a(n1) ~=C

break

end

n1 = n1 + 1

end

%n2按照最大化|E1-E2|的原则选取

E1 = 0

E2 = 0

maxDiff = 0%假设的最大误差

E1 = sum(n1) + b - y(n1)%n1的误差

for i = 1 : n

tempSum = sum(i) + b - y(i)

if abs(E1 - tempSum)>maxDiff

maxDiff = abs(E1 - tempSum)

n2 = i

E2 = tempSum

end

end

%以下进行更新

a1old = a(n1)

a2old = a(n2)

KK = K(n1,n1) + K(n2,n2) - 2*K(n1,n2)

a2new = a2old + y(n2) *(E1 - E2) / KK%计算新的a2

%a2必须满足约束条件

S = y(n1) * y(n2)

if S == -1

U = max(0,a2old - a1old)

V = min(C,C - a1old + a2old)

else

U = max(0,a1old + a2old - C)

V = min(C,a1old + a2old)

end

if a2new >V

a2new = V

end

if a2new <U

a2new = U

end

a1new = a1old + S * (a2old - a2new)%计算新的a1

a(n1) = a1new%更新a

a(n2) = a2new

%更新部分值

sum = zeros(n,1)

for k = 1 : n

for i = 1 : n

sum(k) = sum(k) + a(i) * y(i) * K(i,k)

end

end

Wold = Wnew

Wnew = 0%更新a后的W(a)

tempSum = 0%临时变量

for i = 1 : n

for j = 1 : n

tempSum= tempSum + y(i )*y(j)*a(i)*a(j)*K(i,j)

end

Wnew= Wnew+ a(i)

end

Wnew= Wnew - 0.5 * tempSum

%以下更新b：通过找到某一个支持向量来计算

support = 1%支持向量坐标初始化

while abs(a(support))<1e-4 &&support <= n

support = support + 1

end

b = 1 / y(support) - sum(support)

%判断停止条件

if abs(Wnew/ Wold - 1 ) <= TOL

break

end

end

%输出结果：包括原分类，辨别函数计算结果，svm分类结果

for i = 1 : n

fprintf('第%d点:原标号 ',i)

if i <= 50

fprintf('-1')

else

fprintf(' 1')

end

fprintf('判别函数值%f 分类结果',sum(i) + b)

if abs(sum(i) + b - 1) <0.5

fprintf('1\n')

else if abs(sum(i) + b + 1) <0.5

fprintf('-1\n')

else

fprintf('归类错误\n')

end

end

end

2.名为f的功能函数部分:

function y = k(x1,x2)

y = exp(-0.5*norm(x1 - x2).^2)

end

3.数据：

0.8871 -0.34918.3376 0

1.25191.20836.5041 0

-1.19251.93381.8790 0

-0.12772.43712.6971 0

1.96973.09066.0391 0

0.76030.82411.5323 0

1.63823.55164.4694 0

1.3438 -0.45395.9366 0

-1.3361 -2.02011.6393 0

-0.38863.30418.0450 0

-0.67806.0196 -0.4084 0

0.3552 -0.10511.2458 0

1.65604.07860.8521 0

0.81173.54516.8925 0

1.4773 -1.93403.9256 0

-0.0732 -0.95260.4609 0

0.15214.37112.2600 0

1.48200.74930.3475 0

0.61404.52618.3776 0

0.57213.34603.7853 0

0.52694.14524.3900 0

1.7879 -0.53902.5516 0

0.98855.76250.1832 0

-0.33182.4373 -0.6884 0

1.35785.47093.4302 0

2.7210 -1.12684.7719 0

0.5039 -0.10252.3650 0

1.11071.68853.7650 0

0.78621.35877.3203 0

1.0444 -1.58413.6349 0

1.77951.72764.9847 0

0.67101.4724 -0.5504 0

0.23030.2720 -1.6028 0

1.7089 -1.73994.8882 0

1.00590.55575.1188 0

2.30500.85452.8294 0

1.95550.98980.3501 0

1.71411.54133.8739 0

2.27495.32804.9604 0

1.61710.52703.3826 0

3.6681 -1.84094.8934 0

1.19641.87811.4146 0

0.77882.10480.0380 0

0.79165.09063.8513 0

1.08071.88495.9766 0

0.63402.60303.6940 0

1.9069 -0.06097.4208 0

1.65994.94098.1108 0

1.37630.88993.9069 0

0.84851.46886.7393 0

3.67926.10924.9051 1

4.38127.21486.1211 1

4.39713.41397.7974 1

5.07167.7253 10.5373 1

5.30788.81386.1682 1

4.14485.51562.8731 1

5.36096.04584.0815 1

4.74526.63521.3689 1

6.02746.5397 -1.9120 1

5.31743.01346.7935 1

7.24593.69703.1246 1

6.10078.10875.5568 1

5.99246.92385.7938 1

6.02635.33337.5185 1

3.64708.09156.4713 1

3.65437.22647.5783 1

5.01146.53353.5229 1

4.43487.4379 -0.0292 1

3.60873.73513.0172 1

3.53745.53547.6578 1

6.00482.0691 10.4513 1

3.14234.00035.4994 1

3.40127.15368.3510 1

5.54715.1372 -1.5090 1

6.50895.49118.0468 1

5.45836.76745.9353 1

4.17272.97983.6027 1

5.16728.41364.8621 1

4.88083.55141.9953 1

5.49384.19983.2440 1

5.45425.88034.4269 1

4.87433.96418.1417 1

5.97626.77112.3816 1

6.69457.28581.8942 1

4.73015.76521.6608 1

4.70845.36233.2596 1

6.04083.31387.7876 1

4.60248.35170.2193 1

4.70546.6633 -0.3492 1

4.71395.63626.2330 1

4.0850 10.71183.3541 1

6.10886.16354.2292 1

4.98365.40426.7422 1

6.13876.19492.5614 1

6.07007.03733.3256 1

5.68815.13639.9254 1

7.20582.35704.7361 1

4.29727.32454.7928 1

4.77948.12353.1827 1

3.92826.4092 -0.6339 1

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