Finding Euclidean Distance between two matrices in MATLAB

Question

I am making a Content Based Image Retrieval system for university database. I am using colored features for matching similarity between images. Resolution of my images are 648 * 424 px. Basically what I am doing is:

1. Getting colored histogram of 8 * 8 px blocks of image and concatenating all of these in a single matrix. For this purpose I am using MATLAB function blockproc and code is shown below:

   % Reading two images from Database
   A = imread('bonfire/1.jpg');
   B = imread('bonfire/2.jpg');
   
   % Using blockproc to get feature vector for both images
   C = blockproc(A ,[8, 8], @localHistogram);
   D = blockproc(B, [8, 8], @localHistogram);
   

   While the function localHistogram is:

   function y = localHistogram(block_struct)
   
       % Separating Red, Green and Blue planes
       redPlane    = block_struct.data(:, :, 1);
       greenPlane  = block_struct.data(:, :, 2);
       bluePlane   = block_struct.data(:, :, 3);
   
       % Obtaining histogram for all 3 planes with number of bins = 50
       [pixelCountR, grayLevelsR] = imhist(redPlane, 50);
       [pixelCountG, grayLevelsG] = imhist(greenPlane, 50);
       [pixelCountB, grayLevelsB] = imhist(bluePlane, 50);
   
       % Concatenating three histograms of respective planes
       y = cat(2,pixelCountB, pixelCountG, pixelCountR);
   
   end
   

2. Now I want to find the similarity between the two images by comparing there feature vectors C and D using Euclidean Distance. The dimensions of C and D are 2650 * 243.

What will be the best method to do this with maximum efficiency?