Why the output image is not visible after k means clustering ?

Question

MINO GEORGE 2021-8-31

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1443734-why-the-output-image-is-not-visible-after-k-means-clustering

评论： MINO GEORGE 2021-9-3

Here is the code,

img_folder='C:\Users\COMSOL\Documents\MATLAB\kss';
fname = dir(fullfile(img_folder,'*.jpg'))
grayImage= imread('calculi-140.jpg');
[rows, columns, numberOfColorChannels] = size(grayImage);
  if numberOfColorChannels == 3
  	fprintf('That was a color image.  I am converting it to grayscale.\n');
  	grayImage = rgb2gray(grayImage);
  end
grayImage = imgaussfilt(grayImage);
 gr= imadjust(grayImage,stretchlim(grayImage),[]);
     features = extractLBPFeatures(gr);
numberOfClasses = 3;                                   %k means clustering
 indexes = kmeans(features(:), numberOfClasses);
 classImage = reshape(indexes, size(features));
 figure, imshow(classImage);

I am getting a white linea as the output

The input and output images are attached. Pls check and help me to solve this error. Any help is appreciated.

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KSSV 2021-8-31

在 MATLAB Online 中打开

It is because, you are inputting an array into kmeans.

features = extractLBPFeatures(gr);

Check features, this is 1X59 array.

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Answer 1

Sahil Jain 2021-9-3

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1443734-why-the-output-image-is-not-visible-after-k-means-clustering#answer_779904

Hi. As mentioned by another community member, the "extractLBPFeatures" function returns a vector of features which is why the output of your k-means is also a vector. To not have the output as a white line, you can try using "imshow(classImage, [])". This will display the minimum value of "classImage" as black and the maximum value as white.