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I am developing an image search engine. The engine is meant to retrieve wrist watches based on the input of the user. I am using SIFT descriptors to index the elements in the database and applying Euclidean distance to get the most similar watches. I feel like this type of descriptor is not the best since watches have a similar structure and shape. Right now, the average difference between the best and worst matches is not big enough (15%)

I've been thinking of adding colour to the descriptor, but I'd like to hear other suggestions.

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  • $\begingroup$ Why are you using SIFT? Have you tried deep learning? $\endgroup$ – Brian O'Donnell Dec 25 '18 at 20:52
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How to develop a program that can sort images by similarity is similar to the problem of how to develop a program that can sort words by how similar they look.

For example: "theory" is more similar to "teoryyy" than to "abc".

What determines similarity of two words or images are these factors:

  • how many parts are common to both images

  • how many parts are new in either image

  • how many parts are missing in either image

  • how many parts are zoomed in/out or stretched.

  • how many parts are displaced

There may be some other rules. By combining these rules you can explain how similarity recognition works. This is at the core of intelligence.

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