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Volumn 36, Issue 10, 2014, Pages 1963-1974

Batch-Orthogonal Locality-Sensitive Hashing for Angular Similarity

Author keywords

angular similarity; approximate nearest neighbor search; locality sensitive hashing; Sign random projection

Indexed keywords

ESTIMATION; IMAGE RETRIEVAL; MEAN SQUARE ERROR;

EID: 84933037463     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2014.2315806     Document Type: Article
Times cited : (35)

References (50)
  • 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 Proc. ACM Symp. Theory Comput., 1998, pp. 604-613.
    • (1998) Proc. ACM Symp. Theory Comput. , pp. 604-613
    • Indyk, P.1    Motwani, R.2
  • 3
    • 36949016905 scopus 로고    scopus 로고
    • Randomized algorithms and NLP: Using locality sensitive hash functions for high speed noun clustering
    • D. Ravichandran, P. Pantel, and E. H. Hovy, "Randomized algorithms and NLP: Using locality sensitive hash functions for high speed noun clustering," in Proc. 43rd Annu. Meet. Assoc. Comput. Linguistics, 2005, pp. 622-629.
    • (2005) Proc. 43rd Annu. Meet. Assoc. Comput. Linguistics , pp. 622-629
    • Ravichandran, D.1    Pantel, P.2    Hovy, E.H.3
  • 4
  • 5
    • 84898444828 scopus 로고    scopus 로고
    • Near duplicate image detection: Min-hash and TF-IDF weighting
    • O. Chum, J. Philbin, and A. Zisserman, "Near duplicate image detection: min-hash and TF-IDF weighting," in Proc. Brit. Mach. Vis. Conf., 2008, vol. 810, pp. 812-815.
    • (2008) Proc. Brit. Mach. Vis. Conf. , vol.810 , pp. 812-815
    • Chum, O.1    Philbin, J.2    Zisserman, A.3
  • 9
    • 54749092170 scopus 로고    scopus 로고
    • 80 million tiny images: A large data set for nonparametric object and scene recognition
    • Nov.
    • A. Torralba, R. Fergus, and W. T. Freeman, "80 million tiny images: A large data set for nonparametric object and scene recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 11, pp. 1958-1970, Nov. 2008.
    • (2008) IEEE Trans. Pattern Anal. Mach. Intell. , vol.30 , Issue.11 , pp. 1958-1970
    • Torralba, A.1    Fergus, R.2    Freeman, W.T.3
  • 10
    • 78649317568 scopus 로고    scopus 로고
    • Product quantization for nearest neighbor search
    • Jan.
    • H. Jégou, M. Douze, and C. Schmid, "Product quantization for nearest neighbor search," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 1, pp. 117-128, Jan. 2011.
    • (2011) IEEE Trans. Pattern Anal. Mach. Intell. , vol.33 , Issue.1 , pp. 117-128
    • Jégou, H.1    Douze, M.2    Schmid, C.3
  • 12
    • 0036040277 scopus 로고    scopus 로고
    • Similarity estimation techniques from rounding algorithms
    • M. Charikar, "Similarity estimation techniques from rounding algorithms," in Proc. ACM Symp. Theory Comput., 2002, pp. 380-388.
    • (2002) Proc. ACM Symp. Theory Comput. , pp. 380-388
    • Charikar, M.1
  • 13
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
    • (2004) Int. J. Comput. Vis. , vol.60 , Issue.2 , pp. 91-110
    • Lowe, D.G.1
  • 15
    • 85161990053 scopus 로고    scopus 로고
    • Hashing hyperplane queries to near points with applications to large-scale active learning
    • P. Jain, S. Vijayanarasimhan, and K. Grauman, "Hashing hyperplane queries to near points with applications to large-scale active learning," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 928-936.
    • (2010) Proc. Adv. Neural Inf. Process. Syst. , pp. 928-936
    • Jain, P.1    Vijayanarasimhan, S.2    Grauman, K.3
  • 16
    • 77953184849 scopus 로고    scopus 로고
    • Kernelized locality-sensitive hashing for scalable image search
    • B. Kulis and K. Grauman, "Kernelized locality-sensitive hashing for scalable image search," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 2130-2137.
    • (2009) Proc. IEEE Int. Conf. Comput. Vis. , pp. 2130-2137
    • Kulis, B.1    Grauman, K.2
  • 21
    • 13444291124 scopus 로고    scopus 로고
    • Efficient near-duplicate detection and sub-image retrieval
    • Y. Ke, R. Sukthankar, and L. Huston, "Efficient near-duplicate detection and sub-image retrieval," in Proc. ACM Multimedia, 2004, pp. 869-876.
    • (2004) Proc. ACM Multimedia , pp. 869-876
    • Ke, Y.1    Sukthankar, R.2    Huston, L.3
  • 23
  • 24
    • 0345414073 scopus 로고    scopus 로고
    • Mean shift based clustering in high dimensions: A texture classification example
    • B. Georgescu, I. Shimshoni, and P. Meer, "Mean shift based clustering in high dimensions: A texture classification example," in Proc. IEEE Int. Conf. Comput. Vis., 2003, pp. 456-463.
    • (2003) Proc. IEEE Int. Conf. Comput. Vis. , pp. 456-463
    • Georgescu, B.1    Shimshoni, I.2    Meer, P.3
  • 25
    • 38749118638 scopus 로고    scopus 로고
    • Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
    • A. Andoni and P. Indyk, "Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions," in Proc. Annu. IEEE Symp. Found. Comput. Sci., 2006, pp. 459-468.
    • (2006) Proc. Annu. IEEE Symp. Found. Comput. Sci. , pp. 459-468
    • Andoni, A.1    Indyk, P.2
  • 27
    • 79954525255 scopus 로고    scopus 로고
    • Locality-sensitive binary codes from shift-invariant kernels
    • M. Raginsky and S. Lazebnik, "Locality-sensitive binary codes from shift-invariant kernels," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1509-1517.
    • (2009) Proc. Adv. Neural Inf. Process. Syst. , pp. 1509-1517
    • Raginsky, M.1    Lazebnik, S.2
  • 31
    • 80052874105 scopus 로고    scopus 로고
    • Iterative quantization: A procrustean approach to learning binary codes
    • Y. Gong and S. Lazebnik, "Iterative quantization: A procrustean approach to learning binary codes," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2011, pp. 817-824.
    • (2011) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 817-824
    • Gong, Y.1    Lazebnik, S.2
  • 33
    • 84887359482 scopus 로고    scopus 로고
    • K-means hashing: An affinity-preserving quantization method for learning binary compact codes
    • K. He, F. Wen, and J. Sun, "K-means hashing: An affinity-preserving quantization method for learning binary compact codes," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2013, pp. 2938-2945.
    • (2013) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 2938-2945
    • He, K.1    Wen, F.2    Sun, J.3
  • 35
    • 77956496400 scopus 로고    scopus 로고
    • Sequential projection learning for hashing with compact codes
    • J. Wang, S. Kumar, and S.-F. Chang, "Sequential projection learning for hashing with compact codes," in Proc. Int. Conf. Mach. Learning, 2010, pp. 1127-1134.
    • (2010) Proc. Int. Conf. Mach. Learning , pp. 1127-1134
    • Wang, J.1    Kumar, S.2    Chang, S.-F.3
  • 43
    • 84863752005 scopus 로고    scopus 로고
    • Bayesian locality sensitive hashing for fast similarity search
    • V. Satuluri and S. Parthasarathy, "Bayesian locality sensitive hashing for fast similarity search," in Proc. VLDB Endowment, vol. 5, no. 5, pp. 430-441, 2012.
    • (2012) Proc. VLDB Endowment , vol.5 , Issue.5 , pp. 430-441
    • Satuluri, V.1    Parthasarathy, S.2
  • 45
    • 56749104169 scopus 로고    scopus 로고
    • Hamming embedding and weak geometric consistency for large scale image search
    • H. Jégou, M. Douze, and C. Schmid, "Hamming embedding and weak geometric consistency for large scale image search," in Proc. 10th Eur. Conf. Comput. Vis.: Part I, 2008, pp. 304-317.
    • (2008) Proc. 10th Eur. Conf. Comput. Vis.: Part I , pp. 304-317
    • Jégou, H.1    Douze, M.2    Schmid, C.3
  • 46
    • 84893574327 scopus 로고
    • Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
    • M. X. Goemans and D. P. Williamson, "Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming," J. ACM, vol. 42, no. 6, pp. 1115-1145, 1995.
    • (1995) J. ACM , vol.42 , Issue.6 , pp. 1115-1145
    • Goemans, M.X.1    Williamson, D.P.2
  • 47
    • 84858740468 scopus 로고    scopus 로고
    • Learning to hash with binary reconstructive embeddings
    • B. Kulis and T. Darrell, "Learning to hash with binary reconstructive embeddings," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1042-1050.
    • (2009) Proc. Adv. Neural Inf. Process. Syst. , pp. 1042-1050
    • Kulis, B.1    Darrell, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.