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I have 100 images, all visually similar and I need to search for duplicates. I have an algorithm which can match same colour/pattern/editing etc. but it doesn't support cropping. Meaning if two similar image, one of which is cropped, the result will be different.
I need algorithm which can match two similar images despite of cropping, somehow like tineye works. I got some references but nothing worked.
Here's an example article for reference.
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If you find a cropped image and wonder what's behind the crop, just reverse search the image. You will be able to find plenty of examples of the original along with other cropped results.
To “crop” an image is to remove or adjust the outside edges of an image (typically a photo) to improve framing or composition, draw a viewer's eye to the image subject, or change the size or aspect ratio. In other words, image cropping is the act of improving a photo or image by removing the unnecessary parts.
I think you are on the right track using hashing for identifying nearly-duplicates. I believe locality sensitive hashing can give you the extra mileage you need. It takes into account the locality of the image features from which it computes the hash key and thus achieves better performance for the task of near-duplicates detection.
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