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A Neural Network Approach for Binary Hashing in Image Retrieval

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 533)

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 %.

Keywords

  • Neural network
  • Genetic algorithms
  • Similarity preserving hashing
  • Random projection

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  • DOI: 10.1007/978-3-319-48308-5_38
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Correspondence to Mohamed Moheeb Emara .

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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

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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