Feature Extraction using machine learning

[CODE title="Feature Extraction using machine learning"]clc;clear;close all;



group=[];

data=[];

count = 0;

label = [];



addr = genpath('.'); % generate current path

addpath(addr); % add current folders to search paths



folder=dir('.\dataset');



%%

for mn=3:length(folder)

address=strcat('.\dataset\',folder(mn).name);

label = [label;folder(mn).name];

files=dir(address);

num=numel(files);

% disp(folder(mn).name);

count = count+1;



for i=3:num



% disp(files(i).name);



msg = sprintf('Procecssing Folder %s, File: %d, Filename -> %s\n',folder(mn).name,i-2,files(i).name);

fprintf(msg);



str=strcat('.\dataset\',folder(mn).name,'\',files(i).name);

im=imread(str);

% im = imresize(im,[512,512]);



% imshow(im)



% cform = makecform('srgb2lab');

% J = applycform(im,cform);

% figure;imshow(J);



% K=J,:,2);

% figure;imshow(K);



% L=graythresh(J,:,2));

% BW1=im2bw(J,:,2),L);





gr = rgb2gray(im);

L=graythresh(im);

BW1=im2bw(gr,L);



% figure(1);imshow(BW1);



BW1=bwareaopen(BW1,500);

BB=regionprops(BW1,'Boundingbox');



pause(0.01)



% gr = rgb2gray(im);



object = imcrop(gr,BB(1).BoundingBox);



% figure(2);

imshow(object);



object = imresize(object,[100,50]);

[feat]= hog_feature_vector(object);



group = [group ; count];

data=[data ; feat];

end



end

%%

save HOG_features data group label[/CODE]

thank you bro

very good eline sağlık

Do you do PID control example ?

teşekkürler kraal

Nice, thank you

In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended