how to use SVM classsifier

조회 수: 9(최근 30일)
Keerthi  D
Keerthi D 2020년 12월 6일
댓글: Keerthi D 2020년 12월 10일
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

답변(1개)

Shadaab Siddiqie
Shadaab Siddiqie 2020년 12월 9일
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.
  댓글 수: 1
Keerthi  D
Keerthi D 2020년 12월 10일

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

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