Image Comparison in Matlab [ Matrix Laboratory ] Using Histograms

An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.

Image histograms are present on many modern digital cameras. Photographers can use them as an aid to show the distribution of tones captured, and whether image detail has been lost to blown-out highlights or blacked-out shadows.

Using Matlab We Can get Histogram of any Image : hist()

Syntax :

hist(data) example
hist(data,nbins) example
hist(data,xvalues) example
hist(axes_handle,___) example
nelements = hist(___)
[nelements,centers] = hist(___) example

hist(data) creates a histogram bar plot of data. Elements in data are sorted into 10 equally spaced bins along the x-axis between the minimum and maximum values of data. Bins are displayed as rectangles such that the height of each rectangle indicates the number of elements in the bin.
hist(data,nbins) sorts data into the number of bins specified by nbins.
hist(data,xvalues) uses the values in vector xvalues to determine the bin intervals and sorts data into the number of bins determined by length(xvalues). To specify the bin centers, set xvalues equal to a vector of evenly spaced values. The first and last bins extend to cover the minimum and maximum values in data.
hist(axes_handle,___) plots into the axes specified by axes_handle instead of into the current axes (gca). The option axes_handle can precede any of the input argument combinations in the previous syntaxes.
nelements = hist(___) returns a row vector, nelements, indicating the number of elements in each bin.
[nelements,centers] = hist(___) returns an additional row vector, centers, indicating the location of each bin center on the x-axis. To plot the histogram, you can use bar(centers,nelements).

Simple Program Using Matlab to Compare 1 Image with other 4 Images :

% read two images
Image1 = imread('i1.jpg'); % Image 1
Image2 = imread('i2.jpg'); % Image 2
% convert images to type double (range from from 0 to 1 instead of from 0 to 255)
Imaged1 = im2double(Image1);
Imaged2 = im2double(Image2);
% reduce three channel [ RGB ] to one channel [ grayscale ]
Imageg1 = rgb2gray(Imaged1);
Imageg2 = rgb2gray(Imaged2);
% Calculate the Normalized Histogram of Image 1 and Image 2
hn1 = imhist(Imageg1)./numel(Imageg1);
hn2 = imhist(Imageg2)./numel(Imageg2);
subplot(2,2,1);subimage(Image1)
subplot(2,2,2);subimage(Image2)
subplot(2,2,3);plot(hn1)
subplot(2,2,4);plot(hn2)
% Calculate the histogram error
f = sum((hn1 - hn2).^2);
disp(f) %display the result to console