Use Vectorisation to perform the squared distance between two different arrays

Hi, I'm trying to write a vectorised code to calculate the squared distance between an RGB point and different k Means, and then find the minimum squared distance. suppose that:

RGB= [55;60;100]

Means= [ 209;205;203, 115;110;20, 17;18;20 ]

and squared distance should compare the RGB with each Means(column vector).

for instance,

D1=[(55-209)^2+(60-205)^2+(100-203)^2].

How can I do this without using a loop?

[the cyclist]

Your specification of the Means variable is not going to give a valid array. If you meant it to be a column vector, it should be all semicolons, with no commas. Is that what you intended? Or is it a 3x3 matrix?

Answer 1

[the cyclist]

If I understand what you are trying to do, I would code this as

RGB = [55, 60, 100];

Means = [ 209, 205, 203;
          115, 110,  20;
           17,  18,  20 ]

D = sum((RGB-Means).^2,2)

and not define separate D1, D2, D3 variables.

[ZEE]

suppose we want to compare all the RGB values in an image with each of the k means. will we be able to do this with vectorisation?