Following code is taken form (http://dipwm.blogspot.com/2013/01/svm-support-vector-machine-with-matlab.html) This is very easy to understand but how to show results of classification?
clc;
clear all;
% Load Datasets
Dataset = 'C:\D\Implementation\Dataset';
Testset = 'C:\D\Implementation\Testset';
%load Dataset;
%Load Testset;
% we need to process the images first.
% Convert your images into grayscale
% Resize the images
width=100; height=100;
DataSet = cell([], 1);
for i=1:length(dir(fullfile(Dataset,'*.jpg')))
% Training set process
k = dir(fullfile(Dataset,'*.jpg'));
k = {k(~[k.isdir]).name};
for j=1:length(k)
tempImage = imread(horzcat(Dataset,filesep,k{j}));
imgInfo = imfinfo(horzcat(Dataset,filesep,k{j}));
% Image transformation
if strcmp(imgInfo.ColorType,'grayscale')
DataSet{j} = double(imresize(tempImage,[width height])); % array of images
else
DataSet{j} = double(imresize(rgb2gray(tempImage),[width height])); % array of images
end
end
end
TestSet = cell([], 1);
for i=1:length(dir(fullfile(Testset,'*.jpg')))
% Training set process
k = dir(fullfile(Testset,'*.jpg'));
k = {k(~[k.isdir]).name};
for j=1:length(k)
tempImage = imread(horzcat(Testset,filesep,k{j}));
imgInfo = imfinfo(horzcat(Testset,filesep,k{j}));
% Image transformation
if strcmp(imgInfo.ColorType,'grayscale')
TestSet{j} = double(imresize(tempImage,[width height])); % array of images
else
TestSet{j} = double(imresize(rgb2gray(tempImage),[width height])); % array of images
end
end
end
% Prepare class label for first run of svm
% I have arranged labels 1 & 2 as per my convenience.
% It is always better to label your images numerically
% Please note that for every image in our Dataset we need to provide one label.
% we have 30 images and we divided it into two label groups here.
train_label = zeros(size(8,1),1);
train_label(1:4,1) = 1; % 1 = Airplanes
train_label(5:8,1) = 2; % 2 = Faces
% Prepare numeric matrix for svmtrain
Training_Set=[];
for i=1:length(DataSet)
Training_Set_tmp = reshape(DataSet{i},1, 100*100);
Training_Set=[Training_Set;Training_Set_tmp];
end
Test_Set=[];
for j=1:length(TestSet)
Test_set_tmp = reshape(TestSet{j},1, 100*100);
Test_Set=[Test_Set;Test_set_tmp];
end
% Perform first run of svm
SVMStruct = svmtrain(Training_Set , train_label, 'kernel_function', 'linear');
Group = svmclassify(SVMStruct, Test_Set);
Please help
i hope you are getting logical answer for 'group' variable.
it is simple to show the output as image.
presently you are getting the output in the form of group 1 (logic 0) or group 2 (logic 1) for example.
now you use the if else conditional statement.
if logic 1
print image with airplane.
else logic 0
print image with face
In general it is sufficient to show the error on the test set. this can be a simple bar plot.
i hope you are getting logical answer for 'group' variable.
it is simple to show the output as image.
presently you are getting the output in the form of group 1 (logic 0) or group 2 (logic 1) for example.
now you use the if else conditional statement.
if logic 1
print image with airplane.
else logic 0
print image with face
Hi Shafaq, classification results are not shown in images (unless it's a 2D case and you want to draw the boundary between the samples). Rather, they're usually shown in the form of curves, such as ROC and CMC. Or you can show them in tables.
It depends on what you want to show in the output. If you want to put the label on the images, you can open the image, then print the label on the image and save the overlay-ed version. There are some matlab figure toolbox available online that can help you to do that. If you want to show the output as a table, you can build the confusion matrix and print it as an image. Also you could build a image table with groups of similar label images, This is a bit complicated and you need to make your own code to do that. When the number of images are less, it can be somewhat useful
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