메뉴 건너뛰기




Volumn , Issue , 2011, Pages 1729-1736

Object recognition with hierarchical kernel descriptors

Author keywords

[No Author keywords available]

Indexed keywords

PIXELS; SUPPORT VECTOR MACHINES;

EID: 80052882128     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995719     Document Type: Conference Paper
Times cited : (203)

References (32)
  • 1
    • 85162018819 scopus 로고    scopus 로고
    • Kernel descriptors for visual recognition
    • December, 1729, 1730, 1731, 1732, 1734
    • L. Bo, X. Ren, and D. Fox. Kernel Descriptors for Visual Recognition. In NIPS, December 2010. 1729, 1730, 1731, 1732, 1734
    • (2010) NIPS
    • Bo, L.1    Ren, X.2    Fox, D.3
  • 2
    • 78149331496 scopus 로고    scopus 로고
    • Efficient match kernel between sets of features for visual recognition
    • 1730, 1731
    • L. Bo and C. Sminchisescu. Efficient Match Kernel between Sets of Features for Visual Recognition. In NIPS, 2009. 1730, 1731
    • (2009) NIPS
    • Bo, L.1    Sminchisescu, C.2
  • 3
  • 5
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • 1729, 1734
    • N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 1729, 1734
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 6
    • 85198028989 scopus 로고    scopus 로고
    • ImageNet: A large-scale hierarchical image database
    • 1733
    • J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009. 1733
    • (2009) CVPR
    • Deng, J.1    Dong, W.2    Socher, R.3    Li, L.4    Li, K.5    Fei-fei, L.6
  • 7
    • 77955422240 scopus 로고    scopus 로고
    • Object detection with discriminatively trained part based models
    • 1729
    • P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE PAMI, 32(9):1627-1645, 2009. 1729
    • (2009) IEEE PAMI , vol.32 , Issue.9 , pp. 1627-1645
    • Felzenszwalb, P.1    Girshick, R.2    McAllester, D.3    Ramanan, D.4
  • 8
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • 1729, 1730
    • G. Hinton, S. Osindero, and Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006. 1729, 1730
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.1    Osindero, S.2    Teh, Y.3
  • 9
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • July, 1729
    • G. Hinton and R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786):504-507, July 2006. 1729
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.1    Salakhutdinov, R.2
  • 10
    • 50649113227 scopus 로고    scopus 로고
    • Discriminant embedding for local image descriptors
    • 1730
    • G. Hua, M. Brown, and S. Winder. Discriminant embedding for local image descriptors. In ICCV, 2007. 1730
    • (2007) ICCV
    • Hua, G.1    Brown, M.2    Winder, S.3
  • 11
    • 77953183471 scopus 로고    scopus 로고
    • What is the best multi-stage architecture for object recognition?
    • 1729, 1730
    • K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009. 1729, 1730
    • (2009) ICCV
    • Jarrett, K.1    Kavukcuoglu, K.2    Ranzato, M.3    LeCun, Y.4
  • 12
    • 0032685832 scopus 로고    scopus 로고
    • Using spin images for efficient object recognition in cluttered 3D scenes
    • 1735
    • A. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE PAMI, 21(5), 1999. 1735
    • (1999) IEEE PAMI , vol.21 , Issue.5
    • Johnson, A.1    Hebert, M.2
  • 13
    • 70450177775 scopus 로고    scopus 로고
    • Learning invariant features through topographic filter maps
    • 1729
    • K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun. Learning invariant features through topographic filter maps. In CVPR, 2009. 1729
    • (2009) CVPR
    • Kavukcuoglu, K.1    Ranzato, M.2    Fergus, R.3    LeCun, Y.4
  • 14
    • 77956002520 scopus 로고    scopus 로고
    • Learning multiple layers of features from tiny images
    • 1734
    • A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, 2009. 1734
    • (2009) Technical Report
    • Krizhevsky, A.1
  • 16
    • 85161972005 scopus 로고    scopus 로고
    • Tiled convolutional neural networks
    • 1734
    • Q. Le, J. Ngiam, Z. C. Chia, P. Koh, and A. Ng. Tiled convolutional neural networks. In NIPS. 2010. 1734
    • (2010) NIPS
    • Le, Q.1    Ngiam, J.2    Chia, Z.C.3    Koh, P.4    Ng, A.5
  • 17
    • 84864036295 scopus 로고    scopus 로고
    • Efficient sparse coding algorithms
    • 1730
    • H. Lee, A. Battle, R. Raina, and A. Ng. Efficient sparse coding algorithms. In NIPS, pages 801-808, 2006. 1730
    • (2006) NIPS , pp. 801-808
    • Lee, H.1    Battle, A.2    Raina, R.3    Ng, A.4
  • 18
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • 1730
    • H. Lee, R. Grosse, R. Ranganath, and A. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML, 2009. 1730
    • (2009) ICML
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.4
  • 19
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • 1729, 1730
    • D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60:91-110, 2004. 1729, 1730
    • (2004) IJCV , vol.60 , pp. 91-110
    • Lowe, D.1
  • 20
    • 0036647193 scopus 로고    scopus 로고
    • Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
    • 1731
    • T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE PAMI, 24(7):971-987, 2002. 1731
    • (2002) IEEE PAMI , vol.24 , Issue.7 , pp. 971-987
    • Ojala, T.1    Pietikäinen, M.2    Mäenpää, T.3
  • 21
    • 0029938380 scopus 로고    scopus 로고
    • Emergence of simple-cell receptive field properties by learning a sparse code for natural images
    • 1730
    • B. Olshausen and D. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996. 1730
    • (1996) Nature , vol.381 , pp. 607-609
    • Olshausen, B.1    Field, D.2
  • 22
    • 80052894806 scopus 로고    scopus 로고
    • Descriptor learning for efficient retrieval
    • 1730
    • J. Philbin, M. Isard, J. Sivic, and A. Zisserman. Descriptor learning for efficient retrieval. In ECCV, 2010. 1730
    • (2010) ECCV
    • Philbin, J.1    Isard, M.2    Sivic, J.3    Zisserman, A.4
  • 23
    • 80052914357 scopus 로고    scopus 로고
    • 1732
    • PrimeSense. http://www.primesense.com/. 1732
  • 24
    • 77956000948 scopus 로고    scopus 로고
    • Factored 3-way restricted boltzmann machines for modeling natural images
    • 1734
    • M. Ranzato, K. A., and G. Hinton. Factored 3-way restricted boltzmann machines for modeling natural images. In AISTATS, 2010. 1734
    • (2010) AISTATS
    • Ranzato, M.1    Hinton, G.2
  • 25
    • 77955989954 scopus 로고    scopus 로고
    • Modeling pixel means and covariances using factorized third-order boltzmann machines
    • 1730, 1734
    • M. Ranzato and G. Hinton. Modeling pixel means and covariances using factorized third-order boltzmann machines. In CVPR, 2010. 1730, 1734
    • (2010) CVPR
    • Ranzato, M.1    Hinton, G.2
  • 26
    • 54749092170 scopus 로고    scopus 로고
    • 80 million tiny images: A large data set for nonparametric object and scene recognition
    • 1734
    • A. Torralba, R. Fergus, and W. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE PAMI, 30(11):1958-1970, 2008. 1734
    • (2008) IEEE PAMI , vol.30 , Issue.11 , pp. 1958-1970
    • Torralba, A.1    Fergus, R.2    Freeman, W.3
  • 27
    • 77955996870 scopus 로고    scopus 로고
    • Locality-constrained linear coding for image classification
    • 1730
    • J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Guo. Locality-constrained linear coding for image classification. In CVPR, 2010. 1730
    • (2010) CVPR
    • Wang, J.1    Yang, J.2    Yu, K.3    Lv, F.4    Huang, T.5    Guo, Y.6
  • 28
    • 78149327741 scopus 로고    scopus 로고
    • Kernel methods for deep learning
    • 1730
    • C. Y and L. Saul. Kernel methods for deep learning. In NIPS, 2009. 1730
    • (2009) NIPS
    • Saul, L.1
  • 29
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • 1730
    • Y. B. Y. LeCun, L. Bottou and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 1730
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.B.Y.1    Bottou, L.2    Haffner, P.3
  • 30
    • 70450209196 scopus 로고    scopus 로고
    • Linear spatial pyramid matching using sparse coding for image classification
    • 1729, 1730
    • J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009. 1729, 1730
    • (2009) CVPR
    • Yang, J.1    Yu, K.2    Gong, Y.3    Huang, T.4
  • 31
    • 77956510751 scopus 로고    scopus 로고
    • Improved local coordinate coding using local tangents
    • 1730, 1734
    • K. Yu and T. Zhang. Improved local coordinate coding using local tangents. In ICML, pages 1215-1222, 2010. 1730, 1734
    • (2010) ICML , pp. 1215-1222
    • Yu, K.1    Zhang, T.2
  • 32
    • 84863401481 scopus 로고    scopus 로고
    • Nonlinear learning using local coordinate coding
    • December, 1730
    • K. Yu, T. Zhang, and Y. Gong. Nonlinear Learning using Local Coordinate Coding. In NIPS, December 2009. 1730
    • (2009) NIPS
    • Yu, K.1    Zhang, T.2    Gong, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.