Advertisement
Published in:
2008 | OriginalPaper | Chapter
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
Authors : Neeraj Kumar, Li Zhang, Shree Nayar
Published in: Computer Vision – ECCV 2008
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Many computer vision algorithms require searching a set of images for similar patches, which is a very expensive operation. In this work, we compare and evaluate a number of nearest neighbors algorithms for speeding up this task. Since image patches follow very different distributions from the uniform and Gaussian distributions that are typically used to evaluate nearest neighbors methods, we determine the method with the best performance via extensive experimentation on real images. Furthermore, we take advantage of the inherent structure and properties of images to achieve highly efficient implementations of these algorithms. Our results indicate that vantage point trees, which are not well known in the vision community, generally offer the best performance.