First, aren’t you supposed to be using vl_sift instead of sift? Second, you can use SIFT feature matching to find correspondences in the two images. Here’s some sample code: I = imread(‘p1.jpg’); J = imread(‘p2.jpg’); I = single(rgb2gray(I)); % Conversion to single is recommended J = single(rgb2gray(J)); % in the documentation [F1 D1] = vl_sift(I); … Read more
You should understand that this is not a simple question and you have different concepts you could follow. I will only point out two solution without source-code. Histogram comparison: You could convert both images into grey-scale make a histogram in the range of [0,…,255]. Every pixel-value will be counted. Then use both histograms for comparison. … Read more
I have done something similar, by decomposing images into signatures using wavelet transform. My approach was to pick the most significant n coefficients from each transformed channel, and recording their location. This was done by sorting the list of (power,location) tuples according to abs(power). Similar images will share similarities in that they will have significant … Read more
General idea Option 1: Load both images as arrays (scipy.misc.imread) and calculate an element-wise (pixel-by-pixel) difference. Calculate the norm of the difference. Option 2: Load both images. Calculate some feature vector for each of them (like a histogram). Calculate distance between feature vectors rather than images. However, there are some decisions to make first. Questions … Read more