Abstract
The paper describes the search for a solution to the image near-duplicate detection problem. We assume that there are only two images to compare and classify whether they are near-duplicates. There are some traditional methods to match pair of images, and the evaluation of the most famous of them in terms of the problem is performed in this research. The effective thresholds to separate near-duplicate classes are found during experimental modeling using the INRIA Holidays dataset. The sequence of methods is proposed to make the joint decision better in terms of accuracy. It is shown also that the accuracy of binary classification of the proposed approach for the combination of the histogram comparison and ORB descriptors matching is about 85% for both near-duplicate and not near-duplicate pairs of images. This is compared to the existing methods, and it is shown, that the accuracy of more powerful methods, based on deep learning, is better, but the speed of the proposed method is higher.
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