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Volumn , Issue , 2012, Pages 2424-2431

Chebyshev approximations to the histogram χ 2 kernel

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATIVE APPROXIMATION; CHEBYSHEV POLYNOMIALS; CLASSIFICATION ACCURACY; CONSTANT FACTORS; DATA SETS; FOURIER; FOURIER APPROXIMATIONS; KERNEL CLASSIFIERS; LINEAR TIME; NON-LINEAR MODEL; NON-TRIVIAL; OUT-OF-CORE; PERFORMANCE GAPS; PERFORMANCE IMPROVEMENTS; PERIODIC APPROXIMATION; TESTING DATA; TIME COMPLEXITY; TRAINING EXAMPLE;

EID: 84866664674     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247956     Document Type: Conference Paper
Times cited : (23)

References (30)
  • 1
    • 31844446681 scopus 로고    scopus 로고
    • Predictive low-rank decomposition for kernel methods
    • 2
    • F. Bach and M. I. Jordan. Predictive low-rank decomposition for kernel methods. In ICML, 2005. 2
    • (2005) ICML
    • Bach, F.1    Jordan, M.I.2
  • 3
    • 78149331496 scopus 로고    scopus 로고
    • Efficient match kernels between sets of features for visual recognition
    • 2
    • L. Bo and C. Sminchisescu. Efficient match kernels between sets of features for visual recognition. In NIPS, 2009. 2
    • (2009) NIPS
    • Bo, L.1    Sminchisescu, C.2
  • 5
    • 85011514513 scopus 로고    scopus 로고
    • Monte Carlo and quasi-Monte Carlo methods
    • 4
    • R. Caflisch. Monte Carlo and quasi-Monte Carlo Methods. Acta Mumerica, 7:1-49, 1998. 4
    • (1998) Acta Mumerica , vol.7 , pp. 1-49
    • Caflisch, R.1
  • 6
    • 84861335519 scopus 로고    scopus 로고
    • Object recognition by sequential figure-ground ranking
    • 1, 6
    • J. Carreira, F. Li, and C. Sminchisescu. Object recognition by sequential figure-ground ranking. IJCV, 98, 2012. 1, 6
    • (2012) IJCV , vol.98
    • Carreira, J.1    Li, F.2    Sminchisescu, C.3
  • 7
    • 77956008665 scopus 로고    scopus 로고
    • Constrained parametric min cuts for automatic object segmentation
    • 6
    • J. Carreira and C. Sminchisescu. Constrained parametric min cuts for automatic object segmentation. In CVPR, 2010. 6
    • (2010) CVPR
    • Carreira, J.1    Sminchisescu, C.2
  • 8
    • 84866646669 scopus 로고    scopus 로고
    • Constrained parametric min-cuts for automatic object segmentation
    • 6
    • J. Carreira and C. Sminchisescu. Constrained parametric min-cuts for automatic object segmentation. PAMI, 34, 2012. 6
    • (2012) PAMI , vol.34
    • Carreira, J.1    Sminchisescu, C.2
  • 9
    • 84898420173 scopus 로고    scopus 로고
    • The devil is in the details: An evaluation of recent feature encoding methods
    • 2
    • K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, 2011. 2
    • (2011) BMVC
    • Chatfield, K.1    Lempitsky, V.2    Vedaldi, A.3    Zisserman, A.4
  • 10
    • 80052876786 scopus 로고    scopus 로고
    • What does classifying more than 10,000 image categories tell us?
    • 7, 8
    • J. Deng, A. C. Berg, K. Li, and L. Fei-Fei. What does classifying more than 10,000 image categories tell us? In ECCV, 2010. 7, 8
    • (2010) ECCV
    • Deng, J.1    Berg, A.C.2    Li, K.3    Fei-Fei, L.4
  • 12
    • 29244453931 scopus 로고    scopus 로고
    • On the Nyström method for approximating a gram matrix for improved Kernel-based learning
    • 2
    • P. Drineas and M. Mahoney. On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. JMLR, 6:2153-2175, 2005. 2
    • (2005) JMLR , vol.6 , pp. 2153-2175
    • Drineas, P.1    Mahoney, M.2
  • 14
    • 0041494125 scopus 로고    scopus 로고
    • Efficient svm training using lowrank kernel representation
    • 2
    • S. Fine and K. Scheinberg. Efficient svm training using lowrank kernel representation. JMLR, 2:243-264, 2001. 2
    • (2001) JMLR , vol.2 , pp. 243-264
    • Fine, S.1    Scheinberg, K.2
  • 15
    • 85067032737 scopus 로고    scopus 로고
    • On feature combination for multiclass object classification
    • 4
    • P. V. Gehler and S. Nowozin. On feature combination for multiclass object classification. In ICCV, 2009. 4
    • (2009) ICCV
    • Gehler, P.V.1    Nowozin, S.2
  • 16
    • 84864036295 scopus 로고    scopus 로고
    • Efficient sparse coding algorithms
    • 2
    • H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In NIPS, pages 801-808, 2007. 2
    • (2007) NIPS , pp. 801-808
    • Lee, H.1    Battle, A.2    Raina, R.3    Ng, A.Y.4
  • 17
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • 2
    • H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML, 2009. 2
    • (2009) ICML
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.Y.4
  • 18
    • 77955990360 scopus 로고    scopus 로고
    • Object recognition as ranking holistic figure-ground hypotheses
    • 6
    • F. Li, J. Carreira, and C. Sminchisescu. Object recognition as ranking holistic figure-ground hypotheses. In CVPR, 2010. 6
    • (2010) CVPR
    • Li, F.1    Carreira, J.2    Sminchisescu, C.3
  • 19
    • 84860611691 scopus 로고    scopus 로고
    • Random Fourier approximations for skewed multiplicative histogram kernels
    • 1, 6
    • F. Li, C. Ionescu, and C. Sminchisescu. Random Fourier approximations for skewed multiplicative histogram kernels. In DAGM, 2010. 1, 6
    • (2010) DAGM
    • Li, F.1    Ionescu, C.2    Sminchisescu, C.3
  • 21
    • 80052870284 scopus 로고    scopus 로고
    • Large-scale image classification: Fast feature extraction and svm training
    • 2, 6
    • Y. Lin, F. Lv, S. Zhu, M. Yang, T. Cour, K. Yu, L. Cao, and T. S. Huang. Large-scale image classification: Fast feature extraction and svm training. In CVPR, 2011. 2, 6
    • (2011) CVPR
    • Lin, Y.1    Lv, F.2    Zhu, S.3    Yang, M.4    Cour, T.5    Yu, K.6    Cao, L.7    Huang, T.S.8
  • 22
    • 80052908750 scopus 로고    scopus 로고
    • A coarse-to-fine approach for fast deformable object detection
    • 1
    • M. Pedersoli, A. Vedaldi, and J. Gonzalez. A coarse-to-fine approach for fast deformable object detection. In CVPR, 2011. 1
    • (2011) CVPR
    • Pedersoli, M.1    Vedaldi, A.2    Gonzalez, J.3
  • 23
    • 79959771606 scopus 로고    scopus 로고
    • Improving the fisher kernel for large-scale image classification
    • 2
    • F. Perronnin, J. Sánchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, 2010. 2
    • (2010) ECCV
    • Perronnin, F.1    Sánchez, J.2    Mensink, T.3
  • 24
    • 70350496912 scopus 로고    scopus 로고
    • Principal component analysis for dimension reduction in massive distributed data sets
    • 4
    • Y. Qu, G. Ostrouchov, N. Samatova, and A. Geist. Principal component analysis for dimension reduction in massive distributed data sets. In ICDM, 2002. 4
    • (2002) ICDM
    • Qu, Y.1    Ostrouchov, G.2    Samatova, N.3    Geist, A.4
  • 25
    • 77953218689 scopus 로고    scopus 로고
    • Random features for large-scale kernel machines
    • 1, 2, 3
    • A. Rahimi and B. Recht. Random features for large-scale kernel machines. In NIPS, 2007. 1, 2, 3
    • (2007) NIPS
    • Rahimi, A.1    Recht, B.2
  • 27
    • 77955989063 scopus 로고    scopus 로고
    • Efficient additive kernels via explicit feature maps
    • 1
    • A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In CVPR, 2010. 1
    • (2010) CVPR
    • Vedaldi, A.1    Zisserman, A.2
  • 28
    • 84856194352 scopus 로고    scopus 로고
    • Efficient additive kernels via explicit feature maps
    • 1, 2, 6
    • A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. PAMI, 34:480-492, 2012. 1, 2, 6
    • (2012) PAMI , vol.34 , pp. 480-492
    • Vedaldi, A.1    Zisserman, A.2
  • 29
    • 77955996870 scopus 로고    scopus 로고
    • Locality-constrained linear coding for image classification
    • 2
    • J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained linear coding for image classification. In CVPR, 2010. 2
    • (2010) CVPR
    • Wang, J.1    Yang, J.2    Yu, K.3    Lv, F.4    Huang, T.5    Gong, Y.6
  • 30
    • 84899010839 scopus 로고    scopus 로고
    • Using the Nyström method to speed up Kernel machines
    • 2, 6
    • C. K. I.Williams and M. Seeger. Using the Nyström Method to Speed Up Kernel Machines. In NIPS, 2001. 2, 6
    • (2001) NIPS
    • Williams, C.K.I.1    Seeger, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.