Abstract
Binary encoding methods that keep similarity in large scale data become very used for fast retrieval and effective storage. There have been many recent hashing technics that produce semantic binary codes. We are particularly interested in Spectral Hashing based methods which provide an efficient binary hash codes in a very simple way. This paper presents a comparative experimental study of Spectral Hashing to show the performance gain and the behaviour of this method on large scale Databases. In the best of our knowledge there is no experiments done on the evolution of the hamming matrix size on big data. Two large databases are used to show the limitation of Spectral Hashing and possible research tricks will be proposed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Datar, M., Immorlica, N., Indyk, P., et al.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM, Study (2004)
Weiss, Y., Torralba, A., et al. Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems. pp. 1753–1760 (2009)
He, J., Liu, W., Chang, S.-F.: Scalable similarity search with optimized Kernel Hashing. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [Internet]. ACM, New York, NY, USA (2010)
Shao, J., WU, F., Ouyang, Chuanfei et al. Sparse spectral hashing. Pattern Recogn. Lett. 33(3), 271–277 (2012)
Wang, Q., Zhang, D., Si, L.: Weighted Hashing for fast large scale similarity search. In: Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management [Internet]. ACM, New York, NY, USA (2013)
Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught Hashing for fast similarity search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval [Internet]. ACM, New York, NY, USA (2010)
Weiss, Y., Fergus, R., Torralba, A.: Multidimensional Spectral Hashing. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision ECCV 2012. Springer, Berlin (2012)
Zhuang Y, Liu Y, Wu F, Zhang Y, Shao J. Hypergaph Spectral Hashing for similarity search of social image. In: Proceedings of the 19th ACM International Conference on Multimedia [Internet]. ACM, New York, NY, USA (2011)
Li, P., Wang, M., Cheng, J., Xu, C., Lu, H.: Spectral Hashing with semantically consistent graph for image indexing. IEEE Trans Multimed. 15(1), 141–152 (2013)
Bodó, Z., et al., Csató, L:. Linear spectral hashing. Neurocomputing 141, 117–123 (2014)
Yang, Y., Shen, F., Shen, H.T., et al.: Robust Discrete Spectral Hashing for Large-Scale Image Semantic Indexing (2016)
Bu, J., Tan, S., He, X.: Music recommendation by unified hypergraph: combining social media information and music content. In: ACM Multimedia (2010)
Manning, P.R., Sch¨utze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Karbil, L., Daoudi, I., Medromi, H. (2017). A Comparative Experimental Study of Spectral Hashing. In: El-Azouzi, R., Menasche, D.S., Sabir, E., De Pellegrini, F., Benjillali, M. (eds) Advances in Ubiquitous Networking 2. UNet 2016. Lecture Notes in Electrical Engineering, vol 397. Springer, Singapore. https://doi.org/10.1007/978-981-10-1627-1_35
Download citation
DOI: https://doi.org/10.1007/978-981-10-1627-1_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1626-4
Online ISBN: 978-981-10-1627-1
eBook Packages: EngineeringEngineering (R0)