/**

• Locality-Sensitive Hashing Scheme Based on K-means Double-Bit Quantization.
• For more information on Double-Bit Quantization based LSH, see the following reference.

• Zhu, H. (2014). K-means based double-bit quantization for hashing.
  

• Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP),
  

• 2014 IEEE Symposium on (pp.1-5). IEEE.
  

/ KDBQ, /*

• Locality-Sensitive Hashing Scheme Based on Iterative Quantization.
• For more information on iterative quantization based LSH, see the following reference.

• Gong Y, Lazebnik S, Gordo A, et al. Iterative quantization: A procrustean
  

• approach to learning binary codes for large-scale image retrieval[J].
  

• Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2013,
  

• 35(12): 2916-2929.
  

/ ITQ, /*

• Locality-Sensitive Hashing Scheme Based on Double-Bit Quantization.
• For more information on Double-Bit Quantization based LSH, see the following reference.

• Kong W, Li W. Double-Bit Quantization for Hashing. In AAAI, 2012.
  

• Gong Y, Lazebnik S, Gordo A, et al. Iterative quantization: A procrustean
  

• approach to learning binary codes for large-scale image retrieval[J].
  

• Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2013,
  

• 35(12): 2916-2929.
  

/ DBQ, /*

• Locality-Sensitive Hashing Scheme Based on p-Stable Distributions.
• For more information on p-stable distribution based LSH, see the following reference.

• Mayur Datar , Nicole Immorlica , Piotr Indyk , Vahab S. Mirrokni,
  

• Locality-sensitive hashing scheme based on p-stable distributions,
  

• Proceedings of the twentieth annual symposium on Computational geometry, June
  

• 08-11, 2004, Brooklyn, New York, USA.
  

/ PSD, /*

• Locality-Sensitive Hashing Scheme Based on Random Bits Sampling.
• For more information on random bits sampling based LSH, see the following reference.

• P. Indyk and R. Motwani. Approximate Nearest Neighbor - Towards Removing
  

• the Curse of Dimensionality. In Proceedings of the 30th Symposium on Theory
  

• of Computing, 1998, pp. 604-613.
  

• A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions
  

• via hashing. Proceedings of the 25th International Conference on Very Large
  

• Data Bases (VLDB), 1999.
  

/ RBS, /*

• Locality-Sensitive Hashing Scheme Based on Random Hyperplane.
• For more information on random hyperplane based LSH, see the following reference.

• Charikar, M. S. 2002. Similarity estimation techniques from rounding
  

• algorithms. In Proceedings of the Thiry-Fourth Annual ACM Symposium on
  

• theory of Computing (Montreal, Quebec, Canada, May 19 - 21, 2002). STOC '02.
  

• ACM, New York, NY, 380-388. DOI= http://doi.acm.org/10.1145/509907.509965
  

/ RHP, /*

• Locality-Sensitive Hashing Scheme Based on Spectral Hashing.
• For more information on spectral hashing based LSH, see the following reference.

• Y. Weiss, A. Torralba, R. Fergus. Spectral Hashing.
  

• Advances in Neural Information Processing Systems, 2008.
  

/ SH, /*

• Locality-Sensitive Hashing Scheme Based on Thresholding.
• For more information on thresholding based LSH, see the following reference.

• Zhe Wang, Wei Dong, William Josephson, Qin Lv, Moses Charikar, Kai Li.
  

• Sizing Sketches: A Rank-Based Analysis for Similarity Search. In
  

• Proceedings of the 2007 ACM SIGMETRICS International Conference on
  

• Measurement and Modeling of Computer Systems . San Diego, CA, USA. June
  

• 2007.
  

• Qin Lv, Moses Charikar, Kai Li. Image Similarity Search with Compact
  

• Data Structures. In Proceedings of ACM 13th Conference on Information
  

• and Knowledge Management (CIKM), Washington D.C., USA. November 2004.
  

*/ TH