Abstract
Many image processing algorithms rely on nearest neighbor (NN) or on the k nearest neighbor (kNN) search problem. Several methods have been proposed to reduce the computation time, for instance using space partitionning. However, these methods are very slow in high dimensional space. In this paper, we propose a fast implementation of the brute-force algorithm using GPU (Graphics Processing Units) programming. We show that our implementation is up to 150 times faster than the classical approaches on synthetic data, and up to 75 times faster on real image processing algorithms (finding similar patches in images and texture synthesis).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: IEEE Symposium on Foundations of Computer Science, vol. 51(1), pp. 459–468 (2006)
Angelino, C.V., Debreuve, E., Barlaud, M.: Image restoration using a knn-variant of the mean-shift. In: IEEE International Conference on Image Processing, San Diego, California, USA (October 2008)
Angelino, C.V., Debreuve, E., Barlaud, M.: A nonparametric minimum entropy image deblurring algorithm. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, Nevada, USA (April 2008)
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM 45(6), 891–923 (1998)
Boltz, S., Debreuve, E., Barlaud, M.: High-dimensional statistical distance for region-of-interest tracking: Application to combining a soft geometric constraint with radiometry. In: IEEE International Conference on Computer Vision and Pattern Recognition, Minneapolis, USA (2007)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.: Locality-sensitive hashing scheme based on p-stable distributions. In: Symposium on Computational Geometry, pp. 253–262. ACM Press, New York (2004)
Dudek, R., Cuenca, C., Quintana, F.: Accelerating space variant gaussian filtering on graphics processing unit. In: Moreno DÃaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 984–991. Springer, Heidelberg (2007)
Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: IEEE International Conference on Image Processing, Corfu, Greece, September 1999, pp. 1033–1038 (1999)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: International Conference on Very Large Data Bases, pp. 518–529 (1999)
Heymann, S., Muller, K., Smolic, A., Frohlich, B., Wiegand, T.: Sift implementation and optimization for general-purpose gpu. In: 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2007 (2007)
Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: Symposium on Theory of Computing, pp. 604–613 (1998)
Kumar, N., Zhang, L., Nayar, S.K.: What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images? In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 364–378. Springer, Heidelberg (2008)
Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe lsh: efficient indexing for high-dimensional similarity search. In: VLDB 2007: Proceedings of the 33rd international conference on Very large data bases, pp. 950–961. VLDB Endowment (2007)
Mount, D.M., Arya, S.: Ann: A library for approximate nearest neighbor searching, http://www.cs.umd.edu/~mount/ANN/
Piro, P., Anthoine, S., Debreuve, E., Barlaud, M.: Image retrieval via kullback-leibler divergence of patches of multiscale coefficients in the knn framework. In: IEEE International Workshop on Content-Based Multimedia Indexing, London, UK. IEEE Computer Society, Los Alamitos (2008)
Weber, R., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: International Conference on Very Large Data Bases, pp. 194–205 (1998)
Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcia, V., Nielsen, F. (2009). Searching High-Dimensional Neighbours: CPU-Based Tailored Data-Structures Versus GPU-Based Brute-Force Method. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics CollaborationTechniques. MIRAGE 2009. Lecture Notes in Computer Science, vol 5496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01811-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-01811-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01810-7
Online ISBN: 978-3-642-01811-4
eBook Packages: Computer ScienceComputer Science (R0)