메뉴 건너뛰기




Volumn 37, Issue 11, 2015, Pages 2317-2331

Supervised Hashing Using Graph Cuts and Boosted Decision Trees

Author keywords

binary codes; decision trees; graph cuts; Hashing; image retrieval; nearest neighbour search

Indexed keywords

BINARY CODES; BINS; CLUSTERING ALGORITHMS; CODES (SYMBOLS); DECISION TREES; FORESTRY; GRAPHIC METHODS; HASH FUNCTIONS; IMAGE RETRIEVAL; NEAREST NEIGHBOR SEARCH; OPTIMIZATION; SEARCH ENGINES; TREES (MATHEMATICS);

EID: 84960485177     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2015.2404776     Document Type: Article
Times cited : (96)

References (48)
  • 1
    • 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. 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.3
  • 2
    • 84865410773 scopus 로고    scopus 로고
    • Semi-supervised hashing for large scale search
    • Dec.
    • J. Wang, S. Kumar, and S. Chang, "Semi-supervised hashing for large scale search," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 12, pp. 2393-2406, Dec. 2012.
    • (2012) IEEE Trans. Pattern Anal. Mach. Intell. , vol.34 , Issue.12 , pp. 2393-2406
    • Wang, J.1    Kumar, S.2    Chang, S.3
  • 5
    • 80053457714 scopus 로고    scopus 로고
    • Minimal loss hashing for compact binary codes
    • M. Norouzi and D. Fleet, "Minimal loss hashing for compact binary codes," in Proc. Int. Conf. Mach. Learn., 2011, pp. 353-360.
    • (2011) Proc. Int. Conf. Mach. Learn. , pp. 353-360
    • Norouzi, M.1    Fleet, D.2
  • 6
    • 84887601251 scopus 로고    scopus 로고
    • Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval
    • Dec.
    • Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, "Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 12, pp. 2916-2929, Dec. 2012.
    • (2012) IEEE Trans. Pattern Anal. Mach. Intell. , vol.35 , Issue.12 , pp. 2916-2929
    • Gong, Y.1    Lazebnik, S.2    Gordo, A.3    Perronnin, F.4
  • 11
    • 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
  • 12
    • 80053442434 scopus 로고    scopus 로고
    • The importance of encoding versus training with sparse coding and vector quantization
    • A. Coates and A. Ng, "The importance of encoding versus training with sparse coding and vector quantization," in Proc. Int. Conf. Mach. Learn., 2011, pp. 921-928.
    • (2011) Proc. Int. Conf. Mach. Learn. , pp. 921-928
    • Coates, A.1    Ng, A.2
  • 14
    • 0035509961 scopus 로고    scopus 로고
    • Fast approximate energy minimization via graph cuts
    • Nov.
    • Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
    • (2001) IEEE Trans. Pattern Anal. Mach. Intell. , vol.23 , Issue.11 , pp. 1222-1239
    • Boykov, Y.1    Veksler, O.2    Zabih, R.3
  • 15
    • 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 Recog., 2013, pp. 2938-2945.
    • (2013) Proc. IEEE Conf. Comput. Vis. Pattern Recog. , pp. 2938-2945
    • He, K.1    Wen, F.2    Sun, J.3
  • 19
    • 80455158726 scopus 로고    scopus 로고
    • Learning a nonlinear embedding by preserving class neighbourhood structure
    • R. Salakhutdinov and G. E. Hinton, "Learning a nonlinear embedding by preserving class neighbourhood structure," in Proc. Int. Conf. Artif. Intell. Statist., 2007, pp. 412-419.
    • (2007) Proc. Int. Conf. Artif. Intell. Statist. , pp. 412-419
    • Salakhutdinov, R.1    Hinton, G.E.2
  • 22
    • 84906495994 scopus 로고    scopus 로고
    • Optimizing ranking measures for compact binary code learning
    • G. Lin, C. Shen, and J. Wu, "Optimizing ranking measures for compact binary code learning," in Proc. Eur. Conf. Comput. Vis., 2014, pp. 613-627.
    • (2014) Proc. Eur. Conf. Comput. Vis. , pp. 613-627
    • Lin, G.1    Shen, C.2    Wu, J.3
  • 25
    • 84860241633 scopus 로고    scopus 로고
    • Kernelized locality-sensitive hashing
    • Jun.
    • B. Kulis and K. Grauman, "Kernelized locality-sensitive hashing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 6, pp. 1092-1104, Jun. 2012.
    • (2012) IEEE Trans. Pattern Anal. Mach. Intell. , vol.34 , Issue.6 , pp. 1092-1104
    • Kulis, B.1    Grauman, K.2
  • 29
    • 77956525318 scopus 로고    scopus 로고
    • The elastic embedding algorithm for dimensionality reduction
    • M. A. and C. Perpinan, "The elastic embedding algorithm for dimensionality reduction," in Proc. Int. Conf. Mach. Learn., 2010, pp. 167-174.
    • (2010) Proc. Int. Conf. Mach. Learn. , pp. 167-174
    • Perpinan, C.1
  • 33
    • 0031345518 scopus 로고    scopus 로고
    • Algorithm 778: LBFGS-B: Fortran subroutines for large-scale bound-constrained optimization
    • C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, "Algorithm 778: LBFGS-B: Fortran subroutines for large-scale bound-constrained optimization," ACM Trans. Math. Softw., vol. 23, pp. 550-560, 1997.
    • (1997) ACM Trans. Math. Softw. , vol.23 , pp. 550-560
    • Zhu, C.1    Byrd, R.H.2    Lu, P.3    Nocedal, J.4
  • 35
    • 0000013152 scopus 로고
    • On the statistical analysis of dirty pictures
    • J. Besag, "On the statistical analysis of dirty pictures," J. Roy. Stat. Soc., vol. 48, pp. 259-302, 1986.
    • (1986) J. Roy. Stat. Soc. , vol.48 , pp. 259-302
    • Besag, J.1
  • 37
    • 84869463516 scopus 로고    scopus 로고
    • Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale SVM training
    • Z. Wang, K. Crammer, and S. Vucetic, "Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale svm training," J. Mach. Learn. Res., vol. 13, pp. 3103-3131, 2012.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 3103-3131
    • Wang, Z.1    Crammer, K.2    Vucetic, S.3
  • 38
    • 84897510233 scopus 로고    scopus 로고
    • Quickly boosting decision trees-pruning underachieving features early
    • R. Appel, T. Fuchs, P. Dollar, and P. Perona, "Quickly boosting decision trees-pruning underachieving features early," in Proc. Int. Conf. Mach. Learn., 2013, pp. 594-602.
    • (2013) Proc. Int. Conf. Mach. Learn. , pp. 594-602
    • Appel, R.1    Fuchs, T.2    Dollar, P.3    Perona, P.4
  • 39
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors)
    • J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors)," Ann. Statist., vol. 28, pp. 337-407, 2000.
    • (2000) Ann. Statist. , vol.28 , pp. 337-407
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 46
    • 0035328421 scopus 로고    scopus 로고
    • Modeling the shape of the scene: A holistic representation of the spatial envelope
    • A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," Int. J. Comput. Vis., vol. 42, pp. 145-175, 2001.
    • (2001) Int. J. Comput. Vis. , vol.42 , pp. 145-175
    • Oliva, A.1    Torralba, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.