Unsupervised colour clustering using the k-means algorithm.

 

function call:

[out_img1,out_img2,num1]=vsg('SICluster',in_img1);

 

Arguments:

in_img1 - input image, 3 channel RGB, 1 channel greyscale or binary image.

 

Description:

The function SICluster applies the Unsupervised K-mean clustering for image segmentation.

out_img1 – an image with the same dimensions as the input image which is the input image with a reduced number of colours. The colours are estimated such that the difference between the input and output images is minimised.

out_img2 – an image with the same dimensions as the input image which is an image of the colour labels. i.e. each colour is numbered, and it is these numbers that are output in this image.

The num – an integer indicating the number of colour clusters found.

 

Example:

img = imread('lake.jpg');

h=figure;image(img);set(h,'Name','Input');

[out_img1,out_img2,num1]=vsg('SICluster',img);

h=figure; image(uint8(out_img1));set(h,'Name','SICluster');

h=figure; imagesc((out_img2(:,:,1)));set(h,'Name','SICluster_Label');

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