Abstract
Hashing based approximate nearest neighbor search techniques have attracted considerable attention in media search community. An essential problem of hashing is to keep the neighborhood relationship while doing hashing map. In this paper, we propose a self-organizing map based hashing method–SOMH, which cannot only keep similarity relationship, but also preserve topology of data. Specifically, in SOMH, self-organizing map is introduced to map data points into hamming space. In this framework, in order to make it work well on short and long binary codes, we propose a relaxed version of SOMH and a product space SOMH, respectively. For the optimization problem of relaxed SOMH, we also present an iterative solution. To test the performance of SOMH, we conduct experiments on two benchmark datasets–SIFT1M and GIST1M. Experimental results show that SOMH can outperform or is comparable to several state-of-the-arts.
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Acknowledgments
This work is partially supported by National Natural Science Foundation of China (61173068, 61573212, 61572295), Program for New Century Excellent Talents in University of the Ministry of Education, the Key Science Technology Project of Shandong Province (2014GGD01063), the Independent Innovation Foundation of Shandong Province (2014CGZH1106) and the Shandong Provincial Natural Science Foundation (ZR2014FM020, ZR2014FM031).
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Liang, XL., Xu, XS., Cui, L., Guo, S., Wang, XL. (2016). SOMH: A Self-Organizing Map Based Topology Preserving Hashing Method. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_28
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DOI: https://doi.org/10.1007/978-3-319-27671-7_28
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