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So as the title states, I have a set of images and I want to process input images and need to select the image that "looks" the most like the input image.

I know I've seen something similar where the code could guess who's face was in a picture, I guess I want something like that but for general images.

Sorry if this is a stupid question, but any suggestions or points at resources would be greatly appreciated.

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You will only need to use ML or AI if a) the dataset is very big. b) It is difficult to get the meaning or value of the image.(ex: group of ants, photos of stars in night sky) And many more.

Below is the way I thought it can be done through a machine(with deeplearning, CNN) 1. Train the model arrange the images in dataset into clusters (the number of clusters can be decided based on the criticality of the application, more critical the application more should be the number of clusters) .

  1. Predict about input Then predict the nearest image in the dataset to the provided image.

I recently listened a podcast in which, a problem is approached similarly. The podcast is about youtube predictions and the result is to provide closest video from the youtube videos ( here the dataset).Below is the podcast. ( https://open.spotify.com/episode/6PcMtVXR58i7iu8kLH40Wd?si=feyu0LjESoiE479xHkaqSA )

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