how to find euclidean distance for an image?

Kshitij Singh answered . 2024-12-20 18:48:33

To compute the Euclidean distance between images or image features, your vector length or matrix should have same dimensions. Let say your first image has 1 x 460 vector then your query should be of same length. If that is the case then you can easily find Euclidean distance by the code I have written below. You just have to ensure that the dimensions are the same. I give you example of Histogram feature of two images.

I = imread('myimage.jpg'); 
I = rgb2gray(I);
h = imhist(I); % this will have default bins 256
% now second image 
J = imread('myimage1.jpg'); 
J = rgb2gray(J);
h1 = imhist(J); % this will have default bins 256 
E_distance = sqrt(sum((h-h1).^2));

You can do it for 1000 images as well. Let say now your 1000 images histogram are concatenated into h1. where each column is one histogram. Then your query image histogram is h. Then distance can be computed as follow.

h_new = repmat(h,1,size(h1,2));
E_distance = sqrt(sum((h_new-h1).^2));

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