# Color's histogram and histogram's comparasion.

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Pol am 8 Mai 2023
Kommentiert: Image Analyst am 9 Mai 2023
I've been given a set of images showing different football equipements (t-shirts) with different colors. The idea is, for each image, compute its histogram of RGB colors with N bins, and once computed, compute the similiraty between histograms (using either Chi-Square method or Euclidean distance). I am struggling doing both tasks and I am desperate, so I really hope someone could help me. I'll leave the two functions used down here:
% Histogram computation
function [hist_3d] = Calcular_Histograma_rg(image)
bins = 9;
R = image(:,:,1);
G = image(:,:,2);
B = image(:,:,3);
hist_3d = zeros(bins, bins, bins);
[n m] = size(R);
for i = 1:n
for j = 1:m
r = R(i, j)/ (256/bins) + 1;
b = B(i, j)/ (256/bins) + 1;
g = G(i, j)/ (256/bins) + 1;
if (r > bins)
r = r - 1;
end
if (b > bins)
b = b - 1;
end
if (g > bins)
g = g - 1;
end
hist_3d(r, b, g) = hist_3d(r, b, g) + 1;
end
end
hist_3d = hist_3d ./ (n*m);
hist_3d = hist_3d ./ (n*m);
end
% Similarity using euclidean distance computation
function [similarity] = Calcular_Similitud_rg(hist_org,hist)
[n, m] = size(hist_org);
dist = 0;
for i=1:n
for j=1:m
aux = (hist_org(i, j) - hist(i, j));
dist = dist + (aux*aux);
end
end
similarity = sqrt(dist);
end
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Pol am 8 Mai 2023
Maybe it is better to do it in HSV space. Give the appropiate change as you want.

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### Antworten (2)

Image Analyst am 9 Mai 2023
You're going to have to round r, g, and b because they're not integers and can't be used as indexes.
Don't use image as the name of your variable since it's already a built-in function.
Have you thought about what to do if the two images are not the same size? Maybe you want to normalize the histograms.
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LeoAiE am 9 Mai 2023
Maybe you have implemented the histogram computation and similarity calculation using Euclidean distance. there are a few modifications needed. First, in the Calcular_Histograma_rg function, you have duplicated the normalization step, which should only be done once. Second, you need to adapt the Calcular_Similitud_rg function to handle 3D histograms.
% Histogram computation
function [hist_3d] = Calcular_Histograma_rg(image)
bins = 9;
R = image(:,:,1);
G = image(:,:,2);
B = image(:,:,3);
hist_3d = zeros(bins, bins, bins);
[n, m] = size(R);
for i = 1:n
for j = 1:m
r = floor(R(i, j) / (256/bins)) + 1;
b = floor(B(i, j) / (256/bins)) + 1;
g = floor(G(i, j) / (256/bins)) + 1;
hist_3d(r, b, g) = hist_3d(r, b, g) + 1;
end
end
hist_3d = hist_3d ./ (n * m);
end
% Similarity using Euclidean distance computation
function [similarity] = Calcular_Similitud_rg(hist_org, hist)
[n, m, p] = size(hist_org);
dist = 0;
for i = 1:n
for j = 1:m
for k = 1:p
aux = (hist_org(i, j, k) - hist(i, j, k));
dist = dist + (aux * aux);
end
end
end
similarity = sqrt(dist);
end
hist1 = Calcular_Histograma_rg(image1);
hist2 = Calcular_Histograma_rg(image2);
similarity = Calcular_Similitud_rg(hist1, hist2);
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Image Analyst am 9 Mai 2023
Don't use image as the name of your variable since it's already a built-in function.

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