메뉴 건너뛰기




Volumn , Issue , 2011, Pages 1716-1723

Feature seeding for action recognition

Author keywords

[No Author keywords available]

Indexed keywords

ACTION RECOGNITION; CLASSIFICATION TASKS; LARGE PARTS; LEARNING-BASED METHODS; LIMITED DATA; OVERFITTING; SELECTION METHODS; SYNTHETIC DATA; TRAINING DATA;

EID: 84856639290     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126435     Document Type: Conference Paper
Times cited : (12)

References (30)
  • 1
    • 56449111857 scopus 로고    scopus 로고
    • Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks
    • A. Ahmed, K. Yu, W. Xu, Y. Gong, and E. Xing. Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks. In ECCV, 2008. 2
    • (2008) ECCV , vol.2
    • Ahmed, A.1    Yu, K.2    Xu, W.3    Gong, Y.4    Xing, E.5
  • 2
    • 84864049234 scopus 로고    scopus 로고
    • Analysis of representations for domain adaptation
    • S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In NIPS, 2007. 2
    • (2007) NIPS , vol.2
    • Ben-David, S.1    Blitzer, J.2    Crammer, K.3    Pereira, F.4
  • 4
    • 77955989314 scopus 로고    scopus 로고
    • Cross-dataset action detection
    • L. Cao, Z. Liu, and T. Huang. Cross-dataset action detection. In CVPR, 2010. 2
    • (2010) CVPR , pp. 2
    • Cao, L.1    Liu, Z.2    Huang, T.3
  • 6
    • 78149473088 scopus 로고    scopus 로고
    • The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors
    • G. Carneiro. The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors. In CVPR, 2010. 2
    • (2010) CVPR , pp. 2
    • Carneiro, G.1
  • 9
    • 56049095086 scopus 로고    scopus 로고
    • Boosting for transfer learning
    • W. Dai, Q. Yang, G. Xue, and Y. Yu. Boosting for transfer learning. In ICML, 2007. 2
    • (2007) ICML , vol.2
    • Dai, W.1    Yang, Q.2    Xue, G.3    Yu, Y.4
  • 11
    • 33846622081 scopus 로고    scopus 로고
    • Behavior recognition via sparse spatio-temporal features
    • P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In VS-PETS, 2005. 2
    • (2005) VS-PETS , vol.2
    • Dollar, P.1    Rabaud, V.2    Cottrell, G.3    Belongie, S.4
  • 12
    • 77956003629 scopus 로고    scopus 로고
    • Visual event recognition in videos by learning from web data
    • L. Duan, D. Xu, I. Tsang, and J. Luo. Visual event recognition in videos by learning from web data. In CVPR, 2010. 2
    • (2010) CVPR , vol.2
    • Duan, L.1    Xu, D.2    Tsang, I.3    Luo, J.4
  • 13
  • 14
    • 77954064287 scopus 로고    scopus 로고
    • Object recognition in 3D point clouds using web data and domain adaptation
    • 2
    • K. Lai and D. Fox. Object recognition in 3D point clouds using web data and domain adaptation. International Journal of Robotics Research, 29:1019-1037, 2010. 2
    • (2010) International Journal of Robotics Research , vol.29 , pp. 1019-1037
    • Lai, K.1    Fox, D.2
  • 15
    • 51949083365 scopus 로고    scopus 로고
    • Learning realistic human actions from movies
    • I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008. 2, 3
    • (2008) CVPR , vol.2 , pp. 3
    • Laptev, I.1    Marszalek, M.2    Schmid, C.3    Rozenfeld, B.4
  • 16
    • 84856329825 scopus 로고    scopus 로고
    • Recognizing realistic actions from videos "in the wild"
    • J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos "in the wild". In CVPR, 2009. 5
    • (2009) CVPR , vol.5
    • Liu, J.1    Luo, J.2    Shah, M.3
  • 17
    • 26844513469 scopus 로고    scopus 로고
    • Overfitting in wrapperbased feature subset selection: The harder you try the worse it gets
    • Springer, 2
    • J. Loughrey and P. Cunningham. Overfitting in wrapperbased feature subset selection: The harder you try the worse it gets. In Research and Development in Intelligent Systems XXI, pages 33-43. Springer, 2005. 2
    • (2005) Research and Development in Intelligent Systems , vol.21 , pp. 33-43
    • Loughrey, J.1    Cunningham, P.2
  • 18
    • 79958738498 scopus 로고    scopus 로고
    • Representing pairwise spatial and temporal relations for action recognition
    • P. Matikainen, M. Hebert, and R. Sukthankar. Representing pairwise spatial and temporal relations for action recognition. In ECCV, 2010. 2, 3, 6
    • (2010) ECCV , vol.2 , Issue.3 , pp. 6
    • Matikainen, P.1    Hebert, M.2    Sukthankar, R.3
  • 19
    • 77953182943 scopus 로고    scopus 로고
    • Activity recognition using the velocity histories of tracked keypoints
    • R. Messing, C. Pal, and H. Kautz. Activity recognition using the velocity histories of tracked keypoints. In ICCV, 2009. 2, 3
    • (2009) ICCV , Issue.2 , pp. 3
    • Messing, R.1    Pal, C.2    Kautz, H.3
  • 20
    • 45049084813 scopus 로고    scopus 로고
    • Unsupervised learning of human action categories using spatial-temporal words
    • J. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. IJCV, 79(3), 2008. 2
    • (2008) IJCV , vol.79 , Issue.3 , pp. 2
    • Niebles, J.1    Wang, H.2    Fei-Fei, L.3
  • 21
    • 73449129720 scopus 로고    scopus 로고
    • A highthroughput screening approach to discovering good forms of biologically inspired visual representation
    • N. Pinto, D. Doukhan, J. DiCarlo, and D. Cox. A highthroughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Computational Biology, 5(11), 2009. 3
    • (2009) PLoS Computational Biology , vol.5 , Issue.11 , pp. 3
    • Pinto, N.1    Doukhan, D.2    DiCarlo, J.3    Cox, D.4
  • 22
    • 34948863370 scopus 로고    scopus 로고
    • Surveillance in virtual reality: System design and multi-camera control
    • F. Qureshi and D. Terzopoulos. Surveillance in virtual reality: System design and multi-camera control. In CVPR, 2007. 3
    • (2007) CVPR , vol.3
    • Qureshi, F.1    Terzopoulos, D.2
  • 23
    • 77953218689 scopus 로고    scopus 로고
    • Random features for large-scale kernel machines
    • A. Rahimi and B. Recht. Random features for large-scale kernel machines. In NIPS, 2007. 2
    • (2007) NIPS , vol.2
    • Rahimi, A.1    Recht, B.2
  • 26
    • 80052878786 scopus 로고    scopus 로고
    • Real-time human pose recognition in parts from single depth images
    • J. Shotton, A. Fitzgibbon, M. Cook, and A. Blake. Real-time human pose recognition in parts from single depth images. In In CVPR, 2011. 3
    • (2011) CVPR , vol.3
    • Shotton, J.1    Fitzgibbon, A.2    Cook, M.3    Blake, A.4
  • 27
    • 0035680116 scopus 로고    scopus 로고
    • Rapid object detection using a boosted cascade of simple features
    • P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001. 2
    • (2001) CVPR , vol.2
    • Viola, P.1    Jones, M.2
  • 28
    • 70450188142 scopus 로고    scopus 로고
    • Boosted multi-task learning for face verification with applications to web image and video search
    • X. Wang, C. Zhang, and Z. Zhang. Boosted multi-task learning for face verification with applications to web image and video search. In CVPR, 2009. 2
    • (2009) CVPR , vol.2
    • Wang, X.1    Zhang, C.2    Zhang, Z.3
  • 30
    • 70450164163 scopus 로고    scopus 로고
    • Discriminative subvolume search for efficient action detection
    • J. Yuan, Z. Liu, and Y.Wu. Discriminative subvolume search for efficient action detection. In CVPR, 2009. 5
    • (2009) CVPR , vol.5
    • Yuan, J.1    Liu, Z.2    Wu, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.