Abstract
Nearest neighbor search is known as a challenging issue that has been studied for several decades. Recently, this issue becomes more and more imminent in viewing that the big data problem arises from various fields. According to recent study, graph-based methods are effective to address this issue. However, the k-nearest neighbor graph construction of the existing solutions is computationally inefficient, which becomes the processing bottleneck. To address this issue, a novel k-nearest neighbor graph construction method is proposed. As indicated by extensive experiments, satisfactory performance is achieved on different datasets while the graph construction cost has been reduced to low level. In addition, a comparative study on various approximate nearest neighbor search methods, such as the space partitioning, compressional, hash and the graph-based is presented. As indicated in the experiment, our method makes the best trade-off between search quality and computational cost.
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Notes
- 1.
In our study, the discussion of nearest neighbor search issue is restricted to \(\textit{l}_2\)-distance.
References
Andoni, A.: E2LSH Searching Toolkit. http://www.mit.edu/~andoni/LSH/
Babenko, A., Lempitsky, V.: Additive quantization for extreme vector compression. In: CVPR, pp. 931–938 (2014)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Bentley, J.L.: Multidimensional divide-and-conquer. Commun. ACM 23(4), 214–229 (1980)
Chen, Y., Guan, T., Wang, C.: Approximate nearest neighbor search by residual vector quantization. Sensors 10, 11259–11273 (2010)
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.-T.: NUS-WIDE: a real-world web image database from national university of Singapore. In: ACM International Conference on Image and Video Retrieval (2009)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry. ACM, New York, pp. 253–262 (2004)
Dong, W., Moses, C., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 577–586 (2011). ACM, New York
Fu, C., Cai, D.: EFANNA: an extremely fast approximate nearest neighbor search algorithm based on kNN graph. arXiv.org (2016). arXiv:1609.07228
Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization. Trans. PAMI 36(4), 744–755 (2014)
Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on Management of data, vol. 14, pp. 47–57. ACM, New York, June 1984
Hajebi, K., Abbasi-Yadkor, Y., Shahbazi, H., Zhang, H.: Fast approximate nearest-neighbor search with k-nearest neighbor graph. In: International Joint Conference on Artificial Intelligence, pp. 1312–1317 (2011)
Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. Trans. PAMI 33(1), 117–128 (2011)
Jegou, H., Tavenard, R., Douze, M., Amsaleg, L.: Searching in one billion vectors: re-rank with source coding. In: ICASSP (2011)
Li, W., Zhang, Y., Sun, Y., Wang, W., Zhang, W., Lin, X.: Approximate nearest neighbor search on high dimensional data–experiments, analysis and improvement. Arxiv.org (2016). https://arxiv.org/abs/1610.02455
Malkov, Y.A., Yashunin, D.A.: Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. Arxiv.org (2016). https://arxiv.org/abs/1411.2173
Martinez, J., Hoos, H.H., Little, J.J.: Stacked quantizers for compositional vector compression. Arxiv.org (2014). https://arxiv.org/abs/1411.2173
Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. Trans. PAMI 36, 2227–2240 (2014)
Norouzi, M., Fleet, D.J.: Cartesian k-means. In: CVPR, pp. 3017–3024 (2013)
Verma, N., Kpotufe, S., Dasgupta, S.: Which spatial partition trees are adaptive to intrinsic dimension? In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 565–574 (2009)
Wang, J., Li, S.: Query-driven iterated neighborhood graph search for large scale indexing. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 179–188. ACM, New York (2012)
Wang, J., Wang, J., Zeng, G., Tu, Z., Gan, R., Li, S.: Scalable k-NN graph construction for visual descriptors. In: CVPR, pp. 1106–1113 (2012)
Zhang, T., Du, C., Wang, J.: Composite quantization for approximate nearest neighbor search. In: ICML, pp. 838–846 (2014)
Zhou, W., Yuan, C., Gu, R., Huang, Y.: Large scale nearest neighbors search based on neighborhood graph. In: International Conference on Advanced Cloud and Big Data (2013)
Acknowledgments
This work is supported by National Natural Science Foundation of China under grants 61572408. The authors would like to express their sincere thanks to ODD Concepts Inc. from Seoul, South Korea for their financial support.
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Yang, J., Zhao, WL., Deng, CH., Wang, H., Moon, S. (2018). Fast Nearest Neighbor Search Based on Approximate k-NN Graph. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_32
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