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MATLAB CLASS FOR COMPUTING APPROXIMATE NEAREST NEIGHBORS WRAPPER FOR DAVID M. MOUNT & SUNIL ARYA ANN LIB This wrapper for Matlab was written by Shai Bagon (shai.bagon@weizmann.ac.il). Department of Computer Science and Applied Mathmatics Wiezmann Institute of Science http://www.wisdom.weizmann.ac.il/~bagon The core cpp application was written by David M. Mount and Sunil Arya (available from http://www.cs.umd.edu/~mount/ANN/): ANN is a library for approximate nearest neighbor searching, based on the use of standard and priority search in kd-trees and balanced box-decomposition (bbd) trees. Here are some references to the main algorithmic techniques used here: kd-trees: Friedman, Bentley, and Finkel, ``An algorithm for finding best matches in logarithmic expected time,'' ACM Transactions on Mathematical Software, 3(3):209-226, 1977. Priority search in kd-trees: Arya and Mount, ``Algorithms for fast vector quantization,'' Proc. of DCC '93: Data Compression Conference, eds. J. A. Storer and M. Cohn, IEEE Press, 1993, 381-390. Approximate nearest neighbor search and bbd-trees: Arya, Mount, Netanyahu, Silverman, and Wu, ``An optimal algorithm for approximate nearest neighbor searching,'' 5th Ann. ACM-SIAM Symposium on Discrete Algorithms, 1994, 573-582. ============================================================================================== INSTALLATION 1. Extract all files from archive into a directory (i.e., ~/matlab_tools) 2. Make sure you have thefollowing folders created ~/matlab_tools/ann_wrapper ~/matlab_tools/ann_wrapper/@ann ~/matlab_tools/ann_wrapper/@ann/private 3. Open Matlab 4. Add ~/matlab_tools/ann_wrapper to your path 5. If you haven't use Matlab's compiler yet, you need to set it up: >> mex -setup Matlab detects any compilers installed on your machine and let you choose from them. I suggest using visual studio on Windows machines and gcc on Linux. 6. Go to the ann_wrapper directory >> cd ~/matlab_tools/ann_wrapper 7. Compile the cpp library >> ann_class_compile 8. Test that everythig is properly installed >> test_ann_class 9. If there are no error messages - you are good to go. 10. For help on the class type: >> doc ann ============================================================================================== USING THE WRAPPER ANN class Usage: anno = ann(pts) Input: pts - (d)x(N) matrix of d-dimensional veecotrs representing N points Output: anno - object handle to be used in class' methods Methods: ksearch: k-nn search [idx dst] = ksearch(anno, pt, k, eps, [asm]) idx - indices of matching points: (k)x(#points) dst - square distance of the points anno - active ann object handle pt - query point(s): a (d)x(#points) matrix k - required k aprox. nearest neighbors eps - accuracy multiplicative factor asm - allow self match flag (optional, default: true) prisearch: priority search [idx dst] = ksearch(anno, pt, k, eps, [asm]) see ksearch for inputs/outputs explaination frsearch: fixed radius search [idx dst inr] = frsearch(anno, pt, r, k, eps, [asm]) idx - indices of matching points dst - square distance of the points inr - how many points are in r (range query) anno - active ann object handle pt - query point (only one this time, (d)x1 vector) r - search radius (_not_ squared) k - required k aprox. nearest neighbors. Will return at most k nieghbors of distance at most r. eps - accuracy multiplicative factor asm - allow self match flag (optional, default: true) note that numel(idx) and numel(dst) = min(k, inr) inr might be larger than k reflecting how many points are actually in the search radius. close: you MUST explicitly close the ann class handle when you are done with it. This is crucial for releasing memory and other resources. anno = close(anno); ============================================================================================== This software is provided under the provisions of the Lesser GNU Public License (LGPL). see: http://www.gnu.org/copyleft/lesser.html. This software can be used only for research purposes, you should cite the aforementioned papers in any resulting publication. The Software is provided "as is", without warranty of any kind. ============================================================================================== CITATION If you use this code in a scientific project, you should cite the following works in any resulting publication: @INPROCEEDINGS{Mount1993, author = {Sunil Arya and David M. Mount}, title = {Approximate Nearest Neighbor Queries in Fixed Dimensions}, booktitle = {Proc. 4th Ann. ACM-SIAM Symposium on Discrete Algorithms (SODA)}, year = {1993}, pages = {271-280}, owner = {bagon}, timestamp = {2009.02.04} } @ELECTRONIC{Mount2006, author = {David M. Mount and Sunil Arya}, month = {August}, year = {2006}, title = {ANN: A Library for Approximate Nearest Neighbor Searching}, note = {version 1.1.1}, url = {http://www.cs.umd.edu/~mount/ANN/}, owner = {bagon}, timestamp = {2009.02.04} } @ELECTRONIC{Bagon2009, author = {Shai Bagon}, month = {February}, year = {2009}, title = {Matlab class for ANN}, url = {http://www.wisdom.weizmann.ac.il/~bagon/matlab.html}, owner = {bagon}, timestamp = {2009.02.04} } ==============================================================================================