how to use SVM classsifier

sir.

how to do the SVM classifer(multiclass) to classify four leaf diseases.

here i am doing preprocessing,and then after the segmentation using kmeans clustering

my code is uploded here.please check it and please help me

clc

close all

clear all

[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');

I = imread([pathname,filename]);

I = imresize(I,[256,256]);

% Enhance Contrast

I = imadjust(I,stretchlim(I));

%figure, imshow(I);title('Contrast Enhanced');

% Otsu Segmentation

%I_Otsu = im2bw(I,graythresh(I));

% Conversion to HIS

%I_HIS = rgb2hsi(I);

%% Extract Features

% Color Image Segmentation

% Use of K Means clustering for segmentation

% Convert Image from RGB Color Space to L*a*b* Color Space

% The L*a*b* space consists of a luminosity layer 'L*', chromaticity-layer 'a*' and 'b*'.

% All of the color information is in the 'a*' and 'b*' layers.

cform = makecform('srgb2lab');

% Apply the colorform

lab_he = applycform(I,cform);

% Classify the colors in a*b* colorspace using K means clustering.

% Since the image has 3 colors create 3 clusters.

% Measure the distance using Euclidean Distance Metric.

ab = double(lab_he(:,:,2:3));

nrows = size(ab,1);

ncols = size(ab,2);

ab = reshape(ab,nrows*ncols,2);

nColors = 3;

[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...

'Replicates',3);

%[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3);

% Label every pixel in tha image using results from K means

pixel_labels = reshape(cluster_idx,nrows,ncols);

%figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');

% Create a blank cell array to store the results of clustering

segmented_images = cell(1,3);

% Create RGB label using pixel_labels

rgb_label = repmat(pixel_labels,[1,1,3]);

for k = 1:nColors

colors = I;

colors(rgb_label ~= k) = 0;

segmented_images{k} = colors;

end

figure, subplot(3,1,1);imshow(segmented_images{1});title('Cluster 1'); subplot(3,1,2);imshow(segmented_images{2});title('Cluster 2');

subplot(3,1,3);imshow(segmented_images{3});title('Cluster 3');

set(gcf, 'Position', get(0,'Screensize'));

% Feature Extraction

x = inputdlg('Enter the cluster no. containing the ROI only:');

i = str2double(x);

% Extract the features from the segmented image

seg_img = segmented_images{i};

% Convert to grayscale if image is RGB

if ndims(seg_img) == 3

img = rgb2gray(seg_img);

end

%figure, imshow(img); title('Gray Scale Image');

% Evaluate the disease affected area

black = im2bw(seg_img,graythresh(seg_img));

%figure, imshow(black);title('Black & White Image');

m = size(seg_img,1);

n = size(seg_img,2);

zero_image = zeros(m,n);

%G = imoverlay(zero_image,seg_img,[1 0 0]);

cc = bwconncomp(seg_img,6);

diseasedata = regionprops(cc,'basic');

A1 = diseasedata.Area;

sprintf('Area of the disease affected region is : %g%',A1);

I_black = im2bw(I,graythresh(I));

kk = bwconncomp(I,6);

leafdata = regionprops(kk,'basic');

A2 = leafdata.Area;

sprintf(' Total leaf area is : %g%',A2);

%Affected_Area = 1-(A1/A2);

Affected_Area = (A1/A2);

if Affected_Area < 0.1

Affected_Area = Affected_Area+0.15;

end

sprintf('Affected Area is: %g%%',(Affected_Area*100))

% Create the Gray Level Cooccurance Matrices (GLCMs)

glcms = graycomatrix(img);

% Derive Statistics from GLCM

stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');

Contrast = stats.Contrast

Correlation = stats.Correlation

Energy = stats.Energy

Homogeneity = stats.Homogeneity

Mean = mean2(seg_img)

Standard_Deviation = std2(seg_img)

Entropy = entropy(seg_img)

%RMS = mean2(rms(seg_img));

%Skewness = skewness(img)

Variance = mean2(var(double(seg_img)))

a = sum(double(seg_img(:)));

Smoothness = 1-(1/(1+a))

Kurtosis = kurtosis(double(seg_img(:)))

Skewness = skewness(double(seg_img(:)))

% Inverse Difference Movement

m = size(seg_img,1);

n = size(seg_img,2);

in_diff = 0;

for i = 1:m

for j = 1:n

temp = seg_img(i,j)./(1+(i-j).^2);

in_diff = in_diff+temp;

end

end

Answer 1

[Shadaab Siddiqie]

Here is a SVM resource which might help you to understand MATLAB svm function. Also refere this project for more understanding about classification using svm predict block.

[Keerthi D]

But l can't. Can you help me through the code. Please give me the proper code for my code.