Purpose: To separate images with equal characteristics of a folder with several images
(exp: photo1, photo2, photo3, photo4, photo5>> photo1.Copo1 photo2.Copo2 photo3.Copo3; photo4.Cachorro1, photo5.Cachorro2 ...)
I would like a light on the subject, but in the part that I studied, I believe it would be something in the style: Machine learning - > Unsupervised - > Grouping.
Your question is very generic, there is no way to respond specifically.
One thing you can do is to use k-means to cluster by some similarity criterion. You decide the criteria: 1) Can cluster by color, for example; 2) If the images are normalized, you can use SIFT and set as criteria how many keypoints are inliers.
I'm assuming you do not have any category information from the images, since you mentioned non-supervised learning. If you have any category information, the results are better.
Firstly I would advise reducing the dimensions of these images. For if applying a K-means can happen the problem of Course Of dimensionality that makes with algorithms that use distances between one point and another lose precision. But I do not mean to literally reduce the size of the image but rather use a PCA or SVD for this as it will retain relevant image information.
There are other forms of clusters such as hierarchical clustering and Autoencouders that can be useful as well.
Another important point is the memory required to deal with this amount of images. depending on the algorithm and the amount of memory of your computer you can lock it.
There are more direct methods like comparing pedestrians of image A with image B. (but I do not think it is very good.)
At last there are several ways to do this type of clustering.
