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Volumn , Issue , 2009, Pages 2403-2410

Structured output-associative regression

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PREDICTIVE ANALYTICS; THREE DIMENSIONAL COMPUTER GRAPHICS;

EID: 70450192619     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206699     Document Type: Conference Paper
Times cited : (53)

References (28)
  • 1
    • 33644830736 scopus 로고    scopus 로고
    • Recovering 3d human pose from monocular images
    • A. Agarwal and B. Triggs. Recovering 3d human pose from monocular images. PAMI, 2006.
    • (2006) PAMI
    • Agarwal, A.1    Triggs, B.2
  • 2
  • 3
    • 0036538619 scopus 로고    scopus 로고
    • Shape matching and object recognition using shape contexts
    • S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. PAMI, 24(4):509- 522, 2002.
    • (2002) PAMI , vol.24 , Issue.4 , pp. 509-522
    • Belongie, S.1    Malik, J.2    Puzicha, J.3
  • 4
    • 70350619001 scopus 로고    scopus 로고
    • Learning to localize objects with structured output regression
    • M. Blaschko and C. Lampert. Learning to localize objects with structured output regression. In ECCV, 2008.
    • (2008) ECCV
    • Blaschko, M.1    Lampert., C.2
  • 5
    • 75149166933 scopus 로고    scopus 로고
    • Twin gaussian processes for structured prediction
    • Special Issue on Evaluation of Articulated Human Motion and Pose Estimation
    • L. Bo and C. Sminchisescu. Twin Gaussian Processes for Structured Prediction. IJCV, 2009. Special Issue on Evaluation of Articulated Human Motion and Pose Estimation.
    • (2009) IJCV
    • Bo, L.1    Sminchisescu., C.2
  • 6
    • 51949094782 scopus 로고    scopus 로고
    • Fast algorithms for large scale conditional 3D prediction
    • L. Bo, C. Sminchisescu, A. Kanaujia, and D. Metaxas. Fast Algorithms for Large Scale Conditional 3D Prediction. In CVPR, 2008.
    • (2008) CVPR
    • Bo, L.1    Sminchisescu, C.2    Kanaujia, A.3    Metaxas, D.4
  • 9
    • 29144499905 scopus 로고    scopus 로고
    • Working set selection using second order information for training support vector machines.
    • R. Fan, P. Chen, and C. Lin. Working set selection using second order information for training support vector machines. JMLR, 6, 2005.
    • (2005) JMLR , vol.6
    • Fan, R.1    Chen, P.2    Lin., C.3
  • 10
    • 0002123103 scopus 로고    scopus 로고
    • Dependency networks for inference, collaborative filtering, and data visualization
    • D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering, and data visualization. JMLR, 1:49-75, 2000.
    • (2000) JMLR , vol.1 , pp. 49-75
    • Heckerman, D.1    Chickering, D.2    Meek, C.3    Rounthwaite, R.4    Kadie, C.5
  • 11
    • 51949083415 scopus 로고    scopus 로고
    • Dimensionality reduction using covariance operator inverse regression
    • M. Kim and V. Pavlovic. Dimensionality reduction using covariance operator inverse regression. In CVPR, 2008.
    • (2008) CVPR
    • Kim, M.1    Pavlovic, V.2
  • 12
    • 14344259223 scopus 로고    scopus 로고
    • Discriminative fields for modeling spatial dependencies in natural images
    • S. Kumar and M. Hebert. Discriminative fields for modeling spatial dependencies in natural images. In NIPS, 2003.
    • (2003) NIPS
    • Kumar, S.1    Hebert, M.2
  • 13
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • J. Lafferty, A.Mccallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, pages 282-289, 2001.
    • (2001) ICML , pp. 282-289
    • Lafferty, J.1    Mccallum, A.2    Pereira, F.3
  • 14
    • 14544299611 scopus 로고    scopus 로고
    • On learning vector-valued functions
    • C. Micchelli and M. Pontil. On learning vector-valued functions. Neural Computation, 17(1):177-204, 2005.
    • (2005) Neural Computation , vol.17 , Issue.1 , pp. 177-204
    • Micchelli, C.1    Pontil, M.2
  • 15
    • 50649114839 scopus 로고    scopus 로고
    • The joint manifold model for semi-supervised multi-valued regression
    • R. Navaratnam, A. Fitzgibbon, and R. Cipolla. The Joint Manifold Model for Semi-supervised Multi-valued Regression. In ICCV, 2007.
    • (2007) ICCV
    • Navaratnam, R.1    Fitzgibbon, A.2    Cipolla, R.3
  • 16
    • 70349375765 scopus 로고    scopus 로고
    • Regression on manifolds using kernel dimensionality reduction
    • J. Nilsson, F. Sha, and M. I. Jordan. Regression on Manifolds Using Kernel Dimensionality Reduction. In ICML, 2007.
    • (2007) ICML
    • Nilsson, J.1    Sha, F.2    Jordan, M.I.3
  • 17
    • 51949085389 scopus 로고    scopus 로고
    • Evaluating example-based human pose estimation: Experiments on humaneva sets
    • R. Poppe. Evaluating example-based human pose estimation: Experiments on humaneva sets. In HumanEva Workshop CVPR, 2007.
    • (2007) HumanEva Workshop CVPR
    • Poppe, R.1
  • 18
    • 84898930685 scopus 로고    scopus 로고
    • Learning body pose via specialized maps
    • R. Rosales and S. Sclaroff. Learning Body Pose Via Specialized Maps. In NIPS, 2002.
    • (2002) NIPS
    • Rosales, R.1    Sclaroff., S.2
  • 19
    • 70450199207 scopus 로고    scopus 로고
    • Ridge regression learning algorithm in dual variables
    • C. Saunders, A. Gammerman, and V. Vovk. Ridge regression learning algorithm in dual variables. In ICML, 1998.
    • (1998) ICML
    • Saunders, C.1    Gammerman, A.2    Vovk, V.3
  • 20
    • 77956001488 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 CVPR, 2003.
    • (2003) CVPR
    • Shakhnarovich, G.1    Viola, P.2    Darrell, T.3
  • 22
    • 51949096782 scopus 로고    scopus 로고
    • Predicting 3D people from 2D pictures
    • L. Sigal and M. Black. Predicting 3D people from 2D pictures. In AMDO, 2006.
    • (2006) AMDO
    • Sigal, L.1    Black, M.2
  • 24
    • 33845566915 scopus 로고    scopus 로고
    • Learning joint top-down and bottom-up processes for 3D visual inference
    • C. Sminchisescu, A. Kanaujia, and D. Metaxas. Learning Joint Top-down and Bottom-up Processes for 3D Visual Inference. In CVPR, 2006.
    • (2006) CVPR
    • Sminchisescu, C.1    Kanaujia, A.2    Metaxas, D.3
  • 25
    • 4043137356 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • A. Smola and B. Scholkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199-222, 2004.
    • (2004) Statistics and Computing , vol.14 , Issue.3 , pp. 199-222
    • Smola, A.1    Scholkopf., B.2
  • 26
    • 24944537843 scopus 로고    scopus 로고
    • Large margin methods for structured and interdependent output variables
    • I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 6:1453-1484, 2005.
    • (2005) JMLR , vol.6 , pp. 1453-1484
    • Tsochantaridis, I.1    Joachims, T.2    Hofmann, T.3    Altun, Y.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.