Abstract
Deep convolutional neural networks are widely used to extract high-dimensional features in various image recognition tasks. If the count of classes is relatively large, performance of the classifier for such features can be insufficient to be implemented in real-time applications, e.g., in video-based recognition. In this paper we propose the novel approximate nearest neighbor algorithm, which sequentially chooses the next instance from the database, which corresponds to the maximal likelihood (joint density) of distances to previously checked instances. The Gaussian approximation of the distribution of dissimilarity measure is used to estimate this likelihood. Experimental study results in face identification with LFW and YTF datasets are presented. It is shown that the proposed algorithm is much faster than an exhaustive search and several known approximate nearest neighbor methods.
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Amato, G., Falchi, F., Gennaro, C., Vadicamo, L.: Deep permutations: deep convolutional neural networks and permutation-based indexing. In: Amsaleg, L., Houle, M.E., Schubert, E. (eds.) SISAP 2016. LNCS, vol. 9939, pp. 93–106. Springer, Cham (2016). doi:10.1007/978-3-319-46759-7_7
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: Proceedings of the British Machine Vision, pp. 6–17 (2015)
Wu, X., He, R., Sun, Z.: A lightened CNN for deep face representation. arXiv preprint arXiv:1511.02683 (2015)
Savchenko, A.V.: Search Techniques in Intelligent Classification Systems. Springer International Publishing, Heidelberg (2016)
Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)
Boytsov, L., Naidan, B.: Engineering efficient and effective non-metric space library. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds.) SISAP 2013. LNCS, vol. 8199, pp. 280–293. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41062-8_28
Silpa-Anan, C., Hartley, R.: Optimised KD-trees for fast image descriptor matching. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
He, J., Kumar, S., Chang, S.: On the difficulty of nearest neighbor search. In: 29th International Conference on Machine Learning (ICML-2012), pp. 1127–1134 (2012)
Gonzalez, E.C., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. PAMI 30(9), 1647–1658 (2008)
Savchenko, A.V.: Maximum-likelihood approximate nearest neighbor method in real-time image recognition. Pattern Recogn. 61, 459–469 (2017)
Burghouts, G., Smeulders, A., Geusebroek, J.-M.: The distribution family of similarity distances. In: Advances in Neural Information Processing Systems, pp. 201–208 (2008)
P’kalska, E., Duin, R.P.: Classifiers for dissimilarity-based pattern recognition. In: Proceedings of the 15th IEEE International Conference on Pattern Recognition (ICPR), pp. 12–16 (2000)
Savchenko, A.V.: Clustering and maximum likelihood search for efficient statistical classification with medium-sized databases. Optim. Lett. 11(2), 329–341 (2017)
Acknowledgements
The work is supported by Russian Federation President grant no. MД-306.2017.9 and Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics. The research in Sect. 2 was supported by RSF (Russian Science Foundation) project No. 14-41-00039.
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Savchenko, A.V. (2017). Deep Convolutional Neural Networks and Maximum-Likelihood Principle in Approximate Nearest Neighbor Search. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_5
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DOI: https://doi.org/10.1007/978-3-319-58838-4_5
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