메뉴 건너뛰기




Volumn , Issue , 2009, Pages 1042-1050

Learning to hash with binary reconstructive embeddings

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; HAMMING DISTANCE; LEARNING ALGORITHMS; NEAREST NEIGHBOR SEARCH; SEMANTICS;

EID: 84858740468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (868)

References (17)
  • 1
    • 0036040277 scopus 로고    scopus 로고
    • Similarity estimation techniques from rounding algorithms
    • M. Charikar. Similarity Estimation Techniques from Rounding Algorithms. In STOC, 2002.
    • (2002) STOC
    • Charikar, M.1
  • 2
    • 0031644241 scopus 로고    scopus 로고
    • Approximate nearest neighbors: Towards removing the curse of dimensionality
    • P. Indyk and R. Motwani. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In STOC, 1998.
    • (1998) STOC
    • Indyk, P.1    Motwani, R.2
  • 3
    • 70049096835 scopus 로고    scopus 로고
    • Learning a nonlinear embedding by preserving class neighbourhood structure
    • R. R. Salakhutdinov and G. E. Hinton. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. In AISTATS, 2007.
    • (2007) AISTATS
    • Salakhutdinov, R.R.1    Hinton, G.E.2
  • 5
    • 54749092170 scopus 로고    scopus 로고
    • 80 million tiny images: A large dataset for non-parametric object and scene recognition
    • A. Torralba, R. Fergus, and W. T. Freeman. 80 Million Tiny Images: A Large Dataset for Non-parametric Object and Scene Recognition. TPAMI, 30(11):1958-1970, 2008.
    • (2008) TPAMI , vol.30 , Issue.11 , pp. 1958-1970
    • Torralba, A.1    Fergus, R.2    Freeman, W.T.3
  • 6
    • 84945709355 scopus 로고
    • An algorithm for finding best matches in logarithmic expected time
    • September
    • J. Freidman, J. Bentley, and A. Finkel. An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software, 3(3):209-226, September 1977.
    • (1977) ACM Transactions on Mathematical Software , vol.3 , Issue.3 , pp. 209-226
    • Freidman, J.1    Bentley, J.2    Finkel, A.3
  • 7
    • 84993661659 scopus 로고    scopus 로고
    • M-tree: An efficient access method for similarity search in metric spaces
    • P. Ciaccia, M. Patella, and P. Zezula. M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In VLDB, 1997.
    • (1997) VLDB
    • Ciaccia, P.1    Patella, M.2    Zezula, P.3
  • 9
    • 0026256261 scopus 로고
    • Satisfying general proximity/similarity queries with metric trees
    • J. Uhlmann. Satisfying General Proximity/Similarity Queries with Metric Trees. Information Processing Letters, 40:175-179, 1991.
    • (1991) Information Processing Letters , vol.40 , pp. 175-179
    • Uhlmann, J.1
  • 10
    • 4544259509 scopus 로고    scopus 로고
    • Locality-sensitive hashing scheme based on p-stable distributions
    • M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni. Locality-Sensitive Hashing Scheme Based on p-Stable Distributions. In SOCG, 2004.
    • (2004) SOCG
    • Datar, M.1    Immorlica, N.2    Indyk, P.3    Mirrokni, V.4
  • 11
    • 51949104743 scopus 로고    scopus 로고
    • Fast image search for learned metrics
    • P. Jain, B. Kulis, and K. Grauman. Fast Image Search for Learned Metrics. In CVPR, 2008.
    • (2008) CVPR
    • Jain, P.1    Kulis, B.2    Grauman, K.3
  • 12
    • 34948818208 scopus 로고    scopus 로고
    • Pyramid match hashing: Sub-linear time indexing over partial correspondences
    • K. Grauman and T. Darrell. Pyramid Match Hashing: Sub-Linear Time Indexing Over Partial Correspondences. In CVPR, 2007.
    • (2007) CVPR
    • Grauman, K.1    Darrell, T.2
  • 13
    • 0345414554 scopus 로고    scopus 로고
    • Fast pose estimation with parameter-sensitive hashing
    • G. Shakhnarovich, P. Viola, and T. Darrell. Fast Pose Estimation with Parameter-Sensitive Hashing. In ICCV, 2003.
    • (2003) ICCV
    • Shakhnarovich, G.1    Viola, P.2    Darrell, T.3
  • 15
    • 33749254973 scopus 로고    scopus 로고
    • Photo tourism: Exploring photo collections in 3D
    • New York, NY, USA ACM Press
    • N. Snavely, S. Seitz, and R. Szeliski. Photo Tourism: Exploring Photo Collections in 3D. In SIGGRAPH Conference Proceedings, pages 835-846, New York, NY, USA, 2006. ACM Press.
    • (2006) SIGGRAPH Conference Proceedings , pp. 835-846
    • Snavely, N.1    Seitz, S.2    Szeliski, R.3
  • 16
    • 33745118277 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
    • Washington, D.C., June
    • L. Fei-Fei, R. Fergus, and P. Perona. Learning Generative Visual Models from Few Training Examples: an Incremental Bayesian Approach Tested on 101 Object Categories. In Workshop on Generative Model Based Vision, Washington, D.C., June 2004.
    • (2004) Workshop on Generative Model Based Vision
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 17
    • 51949119257 scopus 로고    scopus 로고
    • Small codes and large databases for recognition
    • A. Torralba, R. Fergus, and Y. Weiss. Small Codes and Large Databases for Recognition. In CVPR, 2008.
    • (2008) CVPR
    • Torralba, A.1    Fergus, R.2    Weiss, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.