/VarianceTestingC

Primary LanguageC++

/**
  • 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