Abstract
Online and cloud storage has become an increasingly popular location to store personal data that led to raising the concerns about storage and retrieval. Similarity-preserving hashing techniques were used for fast storing and retrieval of data. In this paper, a new technique is proposed that uses both randomizing and hashing techniques in a joint structure. The proposed structure uses a Siamese-Twin architecture neural network that applies random projection on data before being used. Furthermore, Particle Swarm Optimization and Genetic Algorithms are used to fine-tune the Siamese-Twin neural network. The proposed technique produces a compact binary code with better retrieval performance than other hashing randomizing technique that varies from 2 % to 5 %.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. VLDB 99(6), 518–529 (1999)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, pp. 1042–1050 (2009)
Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: mij, 1, p. 2 (2011)
Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe LSH: efficient indexing for high-dimensional similarity search. In: The 33rd International Conference on Very Large Data Bases, pp. 950–961. Endowment (2007)
Bawa, M., Condie, T., Ganesan, P.: LSH forest: self-tuning indexes for similarity search. In: The 14th International Conference on World Wide Web, pp. 651–660. ACM (2005)
Dong, W., Wang, Z., Josephson, W., Charikar, M., Li, K.: Modeling LSH for performance tuning. In: 17th ACM Conference on Information and Knowledge Management, pp. 669–678. ACM (2008)
Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Proceedings of the International Conference on Machine Learning, Bellevue, WA, USA, pp. 1–8 (2011)
Gong, Y., Kumar, S., Verma, V., Lazebnik, S.: Angular quantization based binary codes for fast similarity search. In: Advances in Neural Information Processing Systems, Cambridge, MA, USA, vol. 25, pp. 1205–1213. MIT Press (2012)
Norouzi, M., Fleet, D.J., Salakhutdinov, R.R.: Hamming distance metric learning. In: Advances in Neural Information Processing Systems, Cambridge, MA, USA, pp. 1070–107. MIT Press (2012)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)
Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: The Thiry-Fourth Annual ACM Symposium on Theory of Computing, pp. 380–388. ACM (2002)
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 539–546. IEEE (2005)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University Michigan Press, Cambridge (1975)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: The Sixth International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43 (1995)
Grefenstette, J., Gopal, R., Rosmaita, B., Van Gucht, D.: Genetic algorithms for the traveling salesman problem. In: The First International Conference on Genetic Algorithms and Their Applications, pp. 160–168. Lawrence Erlbaum, New Jersey (1985)
Wang, J., Liu, W., Kumar, S., Chang, S.F.: Learning to hash for indexing big data—a survey. IEEE 104(1), 34–57 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Emara, M.M., Fahkr, M.W., Abdelhalim, M.B. (2017). A Neural Network Approach for Binary Hashing in Image Retrieval. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-48308-5_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48307-8
Online ISBN: 978-3-319-48308-5
eBook Packages: EngineeringEngineering (R0)