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Volumn , Issue , 2013, Pages 1737-1744

Heterogeneous image features integration via multi-modal semi-supervised learning model

Author keywords

Heterogeneous Data Integration; Multi Modal Feature Integration; Semi Supervised Learning

Indexed keywords

IMAGING SYSTEMS;

EID: 84898782680     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.218     Document Type: Conference Paper
Times cited : (142)

References (28)
  • 2
    • 80052877826 scopus 로고    scopus 로고
    • Heterogeneous image feature integration via multi-modal spectral clustering
    • X. Cai, F. Nie, H. Huang, and F. Kamangar. Heterogeneous image feature integration via multi-modal spectral clustering. In CVPR, pages 1977-1984, 2011.
    • (2011) In CVPR , pp. 1977-1984
    • Cai, X.1    Nie, F.2    Huang, H.3    Kamangar, F.4
  • 4
    • 79955702502 scopus 로고    scopus 로고
    • Libsvm: A library for support vector machines
    • C.-C. Chang and C.-J. Lin. Libsvm: A library for support vector machines. ACM TIST, 2(3):27, 2011.
    • (2011) ACM TIST , vol.2 , Issue.3 , pp. 27
    • Chang, C.-C.1    Lin, C.-J.2
  • 6
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR (1), pages 886-893, 2005.
    • (2005) In CVPR , vol.1 , pp. 886-893
    • Dalal, N.1    Triggs, B.2
  • 7
    • 36849023115 scopus 로고    scopus 로고
    • A learning framework using green's function and kernel regularization with application to recommender system
    • C. H. Q. Ding, R. Jin, T. Li, and H. D. Simon. A learning framework using green's function and kernel regularization with application to recommender system. In KDD, pages 260-269, 2007.
    • (2007) In KDD , pp. 260-269
    • Ding, C.H.Q.1    Jin, R.2    Li, T.3    Simon, H.D.4
  • 8
    • 50649119439 scopus 로고    scopus 로고
    • Non-metric affinity propagation for unsupervised image categorization
    • D. Dueck and B. J. Frey. Non-metric affinity propagation for unsupervised image categorization. In ICCV, pages 1-8, 2007.
    • (2007) In ICCV , pp. 1-8
    • Dueck, D.1    Frey, B.J.2
  • 9
    • 84932617705 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
    • L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In Workshop on Generative-Model Based Vision, 2004.
    • (2004) In Workshop on Generative-Model Based Vision
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 11
    • 33745855044 scopus 로고    scopus 로고
    • The pyramid match kernel: Discriminative classification with sets of image features
    • K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In ICCV, pages 1458-1465, 2005.
    • (2005) In ICCV , pp. 1458-1465
    • Grauman, K.1    Darrell, T.2
  • 12
    • 70450172710 scopus 로고    scopus 로고
    • Learning to detect unseen object classes by between-class attribute transfer
    • C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, pages 951-958, 2009.
    • (2009) In CVPR , pp. 951-958
    • Lampert, C.H.1    Nickisch, H.2    Harmeling, S.3
  • 13
    • 68849114784 scopus 로고    scopus 로고
    • Foreground focus: Unsupervised learning from partially matching images
    • Y. J. Lee and K. Grauman. Foreground focus: Unsupervised learning from partially matching images. International Journal of Computer Vision, 85(2):143-166, 2009.
    • (2009) International Journal of Computer Vision , vol.85 , Issue.2 , pp. 143-166
    • Lee, Y.J.1    Grauman, K.2
  • 14
    • 34047174674 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
    • F.-F. Li, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 106(1):59-70, 2007.
    • (2007) Computer Vision and Image Understanding , vol.106 , Issue.1 , pp. 59-70
    • Li, F.-F.1    Fergus, R.2    Perona, P.3
  • 15
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
    • (2004) International Journal of Computer Vision , vol.60 , Issue.2 , pp. 91-110
    • Lowe, D.G.1
  • 16
    • 0036647193 scopus 로고    scopus 로고
    • Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
    • T. Ojala, M. Pietik̈ainen, and T. M̈aenp̈äa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):971-987, 2002.
    • (2002) IEEE Trans. Pattern Anal. Mach. Intell. , vol.24 , Issue.7 , pp. 971-987
    • Ojala, T.1    Pietik̈ainen, M.2    M̈aenp̈äa, T.3
  • 17
    • 0035328421 scopus 로고    scopus 로고
    • Modeling the shape of the scene: A holistic representation of the spatial envelope
    • A. Oliva and A. B. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175, 2001.
    • (2001) International Journal of Computer Vision , vol.42 , Issue.3 , pp. 145-175
    • Oliva, A.1    Torralba, A.B.2
  • 21
    • 84863509119 scopus 로고    scopus 로고
    • Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning
    • H. Wang, F. Nie, H. Huang, et al. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics, 28(12):i127-i136, 2012.
    • (2012) Bioinformatics , vol.28 , Issue.12
    • Wang, H.1    Nie, F.2    Huang, H.3
  • 22
    • 33745948591 scopus 로고    scopus 로고
    • Locus: Learning object classes with unsupervised segmentation
    • J. M.Winn and N. Jojic. Locus: Learning object classes with unsupervised segmentation. In ICCV, pages 756-763, 2005.
    • (2005) In ICCV , pp. 756-763
    • Winn, J.M.1    Jojic, N.2
  • 23
    • 51949085938 scopus 로고    scopus 로고
    • Where am i: Place instance and category recognition using spatial pact
    • J. Wu and J. M. Rehg. Where am i: Place instance and category recognition using spatial pact. In CVPR, 2008.
    • (2008) In CVPR
    • Wu, J.1    Rehg, J.M.2
  • 24
    • 0036448175 scopus 로고    scopus 로고
    • Color texture moments for content-based image retrieval
    • H. Yu, M. Li, H. Zhang, and J. Feng. Color texture moments for content-based image retrieval. In ICIP (3), pages 929-932, 2002.
    • (2002) In ICIP , vol.3 , pp. 929-932
    • Yu, H.1    Li, M.2    Zhang, H.3    Feng, J.4
  • 27
    • 35048903652 scopus 로고    scopus 로고
    • Learning from labeled and unlabeled data using random walks
    • D. Zhou and B. Scḧolkopf. Learning from labeled and unlabeled data using random walks. In DAGM-Symposium, pages 237-244, 2004.
    • (2004) In DAGM-Symposium , pp. 237-244
    • Zhou, D.1    Scḧolkopf, B.2
  • 28
    • 1942484430 scopus 로고    scopus 로고
    • Semi-supervised learning using gaussian fields and harmonic functions
    • X. Zhu, Z. Ghahramani, and J. D. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.
    • (2003) In ICML , pp. 912-919
    • Zhu, X.1    Ghahramani, Z.2    Lafferty, J.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.