# Using KNN to Classify a Single Image Example in MATLAB

68 views (last 30 days)
Matpar on 28 Sep 2019
Commented: Matpar on 7 Oct 2019
Hi professionals,
I am grateful for you acknowledging my requests firstly!
I am trying to understand the steps to conduct KNN classification on **One Image**! not a whole data set as it is still challenging for me to comprehend the technical aspects of it in MATLAB!
I manage to get something going and I am not sure if this is what ***The KNN** should be and the Plotted area specified is not showing!
Can a professional take a look at this and see where I took a wrong turn please? I am not certain of the stages of doing the KNN process but I gave it my best shot!!
If you have an example of how to conduct this process step by step somewhere, It would mean the world to me to grasp the understand, just point me to the location or direction and I will try for myself..
Hope I had something at least right in my example!!
I would ever be grateful!
My CODE:
%Step 1
figure; imshow(im);
[r,c,s]=size(im);
classes={'Navy Blue','Cocoa Brown','Grey','Silver','White','Brown','Light Green'};
numberofClasses=length(classes); %The Number of The Classes Would be 7 Colours
sample_regions = false([r c numberofClasses]); %initialise a matrics for sample regions not that it is all equal to zeros denoted by the syntax
%Step 2 Selecting the sample regions
figim = figure;
for count=1:numberofClasses % Selecting the sample regions for every 7 class that was created
set(figim,'name',['Please Select The Sample Region For' classes{count}]);
sample_regions(:,:,count)=roipoly(im);
end
close(figim);
%Step 3 After selecting the sample regions it is displayed via the loop
for count=1:numberofClasses
figure
imshow(sample_regions(:,:,count))
title(['The Sample Region For' classes{count}]);
end
%Step 4 Convert RGB image to L*a*B image
cform = makecform('srgb2lab');
lab_im=applycform(im,cform);
%Step 5 Once the L*a*B Values were calculate The (MEAN) for the (a*) and (*b)
a=lab_im(:,:,2);
b=lab_im(:,:,3);
color_markers= repmat(0, [numberofClasses, 2]); %This matrix will highlight the values
%of the (MEAN of the a & b varibles for all of the 7 classes that was selected)
for count=1:numberofClasses
color_markers(count,1)=mean2(a(sample_regions(:,:,count)));
color_markers(count,2)=mean2(b(sample_regions(:,:,count)));
end
%Step 6 Classify Each Pixel Using The Nearest Neighbour Rule
color_labels=0:numberofClasses-1;
distance=repmat(0, [size(a), numberofClasses]);
%%Step 7 Performing the Classifiation
for count=1:numberofClasses %iterating from 1 through the 7 classes
distance(:,:,count)=((a-color_markers(count,1)).^2+(b-color_markers(count,2)).^2).^0.5;
end
[value, label]=min(distance,[],3);
label=color_labels(label);
colors=[0.1686 0.2235 1.0000; 0.2784 0.1608 0.0824; 0.5 0.5 0.5; 0.7294 0.7216 0.7216; 1 1 1; 0.5216 0.4196 0.3843;...
0.4667 0.6745 0.1882];
y=zeros(size(im));
l=double(label)+1;
for m=1:r
for n=1:c
y(m,n,:)=colors(l(m,n),:);
end
end
figure; imshow(y)
colorbar
%Step 8 Scatter plot for the nearest neighbour classification
purple = [119/255 73/255 152/255];
plot_labels = {'k', 'r', 'g', purple, 'm', 'y'};
figrue;
%Step 9 plotting the values utilising the standard plot parameters
for count=1:numberofClasses
plot(a(label==count-1),b(label==count-1), '.','MarkerEdgeColor', plot_labels{count}, 'MarkerFaceColor',...
plot_labels{count});
hold on
end
%Step 10 Display The Plotted Areas
title('Scatterplot of the segmented pixels in ''a*b*'' space');
llabel('''a*'' values');
ylabel('''b*'' values');
%% Thank You So Very Much!!
Image Analyst on 28 Sep 2019
You forgot to attach car.jpg. I'll check back later in the day for it.
Matpar on 28 Sep 2019
My apologies IA,
I know you do this so fast one day i will get the gist of these processes!

Image Analyst on 30 Sep 2019
Attached is a demo for a discriminant classifier. It should be easy for you to adapt it to KNN.
##### 3 CommentsShow 1 older commentHide 1 older comment
Matpar on 4 Oct 2019
I think i am getting mixed up with classifier by Knn and clustering by knn
Image Analyst on 5 Oct 2019
It does classification according to similar colors. Those colored regions may or may not represent foreground and background as far as distance from the camera.

Image Analyst on 30 Sep 2019
See attached demo for using KNN classification to classify an RGB image into the number of classes you specify. Adapt as needed.
Image Analyst on 6 Oct 2019
Matpar on 7 Oct 2019
ok kool thank you a mill. appreciate this loads