The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January (2011 vol.33)
pp: 117-128
Hervé Jégou , INRIA Rennes, Rennes
Cordelia Schmid , INRIA Rhône-Alpes, Saint Ismier
ABSTRACT
This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.
INDEX TERMS
High-dimensional indexing, image indexing, very large databases, approximate search.
CITATION
Hervé Jégou, Cordelia Schmid, "Product Quantization for Nearest Neighbor Search", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 1, pp. 117-128, January 2011, doi:10.1109/TPAMI.2010.57
REFERENCES
[1] K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, "When Is 'Nearest Neighbor' Meaningful?" Proc. Int'l Conf. Database Theory, pp. 217-235, Aug. 1999.
[2] C. Böhm, S. Berchtold, and D. Keim, "Searching in High-Dimensional Spaces: Index Structures for Improving the Performance of Multimedia Databases," ACM Computing Surveys, vol. 33, pp. 322-373, Oct. 2001.
[3] J. Friedman, J.L. Bentley, and R.A. Finkel, "An Algorithm for Finding Best Matches in Logarithmic Expected Time," ACM Trans. Math. Software, vol. 3, no. 3, pp. 209-226, 1977.
[4] R. Weber, H.-J. Schek, and S. Blott, "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces," Proc. Int'l Conf. Very Large DataBases, pp. 194-205, 1998.
[5] M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni, "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions," Proc. Symp. Computational Geometry, pp. 253-262, 2004.
[6] A. Gionis, P. Indyk, and R. Motwani, "Similarity Search in High Dimension via Hashing," Proc. Int'l Conf. Very Large DataBases, pp. 518-529, 1999.
[7] M. Muja and D.G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration," Proc. Int'l Conf. Computer Vision Theory and Applications, 2009.
[8] B. Kulis and K. Grauman, "Kernelized Locality-Sensitive Hashing for Scalable Image Search," Proc. Int'l Conf. Computer Vision, Oct. 2009.
[9] G. Shakhnarovich, T. Darrell, and P. Indyk, Nearest-Neighbor Methods in Learning and Vision: Theory and Practice, ch. 3. MIT Press, Mar. 2006.
[10] Y. Ke, R. Sukthankar, and L. Huston, "Efficient Near-Duplicate Detection and Sub Image Retrieval," Proc. ACM Int'l Conf. Multimedia, pp. 869-876, 2004.
[11] B. Matei, Y. Shan, H. Sawhney, Y. Tan, R. Kumar, D. Huber, and M. Hebert, "Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1111-1126, July 2006.
[12] C. Silpa-Anan and R. Hartley, "Optimized KD-Trees for Fast Image Descriptor Matching," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[13] D. Nistér and H. Stewénius, "Scalable Recognition with a Vocabulary Tree," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2161-2168, 2006.
[14] A. Torralba, R. Fergus, and W.T. Freeman, "80 Million Tiny Images: A Large Database for Non-Parametric Object and Scene Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1958-1970, Nov. 2008.
[15] A. Torralba, R. Fergus, and Y. Weiss, "Small Codes and Large Databases for Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[16] A. Oliva and A. Torralba, "Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope," Int'l J. Computer Vision, vol. 42, no. 3 pp. 145-175, 2001.
[17] Y. Weiss, A. Torralba, and R. Fergus, "Spectral Hashing," Proc. Advances in Neural Information Processing Systems 2008.
[18] H. Jégou, M. Douze, and C. Schmid, "Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search," Proc. European Conf. Computer Vision, Oct. 2008.
[19] M. Douze, H. Jégou, H. Singh, L. Amsaleg, and C. Schmid, "Evaluation of GIST Descriptors for Web-Scale Image Search," Proc. Int'l Conf. Image and Video Retrieval, 2009.
[20] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[21] D. Lowe, "Distinctive Image Features from Scale Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2 pp. 91-110, 2004.
[22] R.M. Gray and D.L. Neuhoff, "Quantization," IEEE Trans. Information Theory, vol. 44, no. 10, pp. 2325-2384, Oct. 1998.
[23] D.E. Knuth, The Art of Computer Programming, Sorting and Searching, second ed., vol. 3. Addison Wesley, 1998.
[24] J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," Proc. Int'l Conf. Computer Vision, pp. 1470-1477, 2003.
[25] M. Perdoch, O. Chum, and J. Matas, "Efficient Representation of Local Geometry for Large Scale Object Retrieval," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2009.
[26] H. Jégou, M. Douze, and C. Schmid, "Packing Bag-of-Features," Proc. Int'l Conf. Computer Vision, Sept. 2009.
[27] H. Jégou, H. Harzallah, and C. Schmid, "A Contextual Dissimilarity Measure for Accurate and Efficient Image Search," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[28] H. Cho, I. Dhillon, Y. Guan, and S. Sra, "Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data," Proc. SIAM Int'l Conf. Data Mining, pp. 114-125, Apr. 2004.
[29] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Object Retrieval with Large Vocabularies and Fast Spatial Matching," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
14 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool