K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
Nearest neighbor searching and applications
Nearest neighbor searching and applications
An optimal algorithm for approximate nearest neighbor searching
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Digital Image Processing
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The most frequently occurring problem in image retrieval is find-the-similar-image, which in general is finding the nearest neighbor. From the literature, it is well known that k-d trees are efficient methods of finding nearest neighbors in high dimensional spaces. In this paper we survey the relevant k-d tree literature, and adapt the most promising solution to the problem of image retrieval by finding the best parameters for the bucket size and threshold. We also test the system on the Corel Studio photo database of 18,724 images and measure the user response times and retrieval accuracy.