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Image Classification - Black and White, Color

One of the classic problems is classifying the images. There will be series of posts on this topic which will be closely related to machine learning concepts. Though there are different techniques available in the scientific community and market, let’s start with the basic method of classification - decision tree. Look at the dataset,
Channel
Variance
Image Type
Monochrome
low
BW
RGB
low
BW
RGB
High
Color

There is only two types of classification ‘Black and White’ or ‘Color’
MATLAB CODE:
%Image Classification: BW and Color Image
%List of 'jpeg' images
fname = dir('*.jpeg');

for k=1:size(fname,1)
   
    %Read the image
    Img = imread(fname(k).name);
    %Size of the Image
    mn=size(Img,1)*size(Img,2);
   
    %Estimate the difference along the Channels
    VarI=std(double(Img),0,3);
    BelowT = sum(VarI(:)<=25);
    Prob_BT= BelowT/mn;
   
    if(Prob_BT==1)
        figure(1),imshow(Img);title('Black and White Image');
    else
         figure(2),imshow(Img);title('Color Image');
    end
   
   
  
end

The key feature is the ‘Variance’ that ensures how different each value along the Red, Green and Blue Channels at each pixel location.


For instance, at a particular pixel location if Red channel = 30 , Green channel = 30 and Blue channel = 30 then the variance will be zero which infers that the image is grayscale or BW. Also, we can tend to infer that the image as black and white even when the pixel values in these channels are close to each other.



If we look at the car image and its corresponding variance map, we can see that the pixel values of the RGB channels at a particular pixel location varies greatly and thus the variance map shows this huge difference.  We can say that the picture has been classified correctly.

All the RGB channels have the same value with respect to each pixel location which indicates that the image is gray scale. And variance map indicates zero for the whole image. So the Cat image is BW type.

This zebra image which is a natural black and white image even though its RGB components are not same but they are close to each other and the variance map has done a good job to check the closeness between the RGB channels and it has been classified as BW.


The last one is the swan image. On a first glance it looks like a black and white image and I would like to classify it as BW but the variance map tells a different story. We can see the beak region has a high variance where the RGB components differ greatly while the remaining part of the image is almost having RGB components close to each other. 

The variance map has been quite useful feature but maybe we need to classify the image not as ‘Color Image’ but ‘Black and White dominant Image’








EXAMPLE 2:

Add more features and more classification to make the data set little bit complicated and messy. We have obtained the decision tree for the below mentioned data set from the article on ‘DecisionTree – supervised Learner’. Let’s use it to check out the results.

Channel
Variance
DominantB
ImageType
Monochrome
low
High
Black
Monochrome
low
low
White
Monochrome
low
Medium
BW
RGB
low
High
Black
RGB
low
low
White
RGB
low
Medium
BW
RGB
High
High
Color
RGB
High
low
Color

MATLAB CODE:
%Image Classification: BW, Black Dominant Image, White Dominant Image, Color Image
%List of 'jpeg' images
fname = dir('*.jpeg');

for k = 1:size(fname,1)
   
    %Read the image
    Img = imread(fname(k).name);
    %size of the image
    mn = size(Img,1)*size(Img,2);
   
    %Compute Variance
     VarI = std(double(Img),0,3);
     BelowT = sum(VarI(:)<=14);
     Prob_BT = BelowT/mn; %Number of values below the Threshold
       
       
      T1 = (Img(:,:,1) < 100&Img(:,:,2) < 100&Img(:,:,3) < 100);%Black or gray pixels
      T2 = (Img(:,:,1) > 150&Img(:,:,2) > 150&Img(:,:,3) > 150);%White pixels
      
      T1 = (sum(T1(:)) / mn)*100;
      T2 = (sum(T2(:)) / mn)*100;
     
      if((T1/T2) > 0.8 & (T1/T2) < 1.6) %Medium Criteria for the feature 'DominantB'
        
              figure(1),imshow(Img);title('Image Type:Black and White Image','FontSize',20);
             
         
         
      %When the number of black pixels are high   
      elseif(T1 > T2) %High Criteria for the feature 'DominantB'
          %Variance Low
          if(Prob_BT > 0.6)
              figure(1),imshow(Img);title('Image Type:Black Dominant Image','FontSize',20);
            
          else
              figure(1),imshow(Img);title('Image Type:Color Image','FontSize',20);
             
          end
       %When the number of white pixels are high  
      elseif(T2 > T1)%Low Criteria for the feature 'DominantB'
          %Variance Low
          if(Prob_BT > 0.6)
              figure(1),imshow(Img);title('Image Type:White Dominant Image','FontSize',20);
              
          else
              figure(1),imshow(Img);title('Image Type:Color Image','FontSize',20);
              
          end
      end

    %To visualize each image
    pause
   
end


EXPLANATION:


The number of gray or dark pixels is estimated. Similarly, the number of white or light pixels is estimated. If the ratio between these two is almost equal then the image is black and white. If the gray pixels are more than the white pixels then estimate the variance. Based on the variance map, decide whether the image is color image or black dominant image. In the similar manner, if the white pixels are more than the gray pixels then based on the variance map decide whether the image type is color or white dominant image. The result below illustrates the classification done on the images and how they were classified.




The images used for the classification were taken from website the https://pixabay.com/
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