Representing actions with Kernels

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2011, Pages 2028-2035

  Luo, Guoliang ^a   Bergström, Niklas ^a   Ek, Carl Henrik ^a   Kragic, Danica ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC ENVIRONMENTS; HIGH-DIMENSIONAL; HUMAN ACTIONS; RESEARCH GOALS; SENSORY DATA;

ROBOTICS;

INTELLIGENT ROBOTS;

EID: 84455207416     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2011.6048175     Document Type: Conference Paper

References (21)

1. C. Bishop, Pattern recognition and machine learning. Springer New York, 2006, vol. 4.
2. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
3. D. Lowe, "Distinctive image features from scale-invariant keypoints,"International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
4. N. Dalal, B. Triggs, I. Rhone-Alps, and F. Montbonnot, "Histograms of oriented gradients for human detection," Computer Vision and Pattern Recognition, 2005.
5. S. Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, 2002. (Pubitemid 34448769)
6. A. Kanaujia, C. Sminchisescu, and D. N. Metaxas, "Semi-supervised hierarchical models for 3d human pose reconstruction," in Computer Vision and Pattern Recognition, 2007.
7. E. Aksoy, A. Abramov, F. Wörgötter, and B. Dellen, "Categorizing object-action relations from semantic scene graphs," in International Conference on Robotics and Automation. IEEE, 2010, pp. 398-405.
8. J. Shawe-Taylor and N. Cristianini, Kernel methods for pattern analysis. Cambridge Univ Pr, 2004.
9. Y. Boykov and M. Jolly, "Interactive graph cuts for optimal boundary and region segmentation of objects in ND images," in International Conference on Computer Vision. Vancouver, BC, Canada, 2001, pp. 105-112.
10. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, 2002.
11. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "From contours to regions: An empirical evaluation," in Proc. CVPR, 2009.
12. B. Dellen, E. Erdal Aksoy, and F. Wörgötter, "Segment tracking via a spatiotemporal linking process including feedback stabilization in an nD lattice model," Sensors, vol. 9, no. 11, pp. 9355-9379, 2009.
13. B. Dellen and F. Wörgötter, "Disparity from stereo-segment silhouettes of weakly-textured images," British Machine Vision Conference, 2009.
14. N. Cristianini and J. Shawe-Taylor, An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge Univ Pr, 2000.
15. J. Mercer, "Functions of positive and negative type, and their connection with the theory of integral equations," Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, pp. 415-446, 1909.
16. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.
17. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins, "Text classification using string kernels," The Journal of Machine Learning Research, vol. 2, pp. 419-444, 2002. (Pubitemid 135712568)
18. M. Björkman and D. Kragic, "Active 3d scene segmentation and detection of unknown objects," in International Conference on Robotics and Automation, 2010.
19. -, "Active 3d segmentation through fixation of previously unseen objects," in British Machine Vision Conference, 2010.
20. H. Tschach, C. Saunders, and J. Shawe-Taylor, "Syllables and other string kernel extensions," in Proceedings of ICML'02, 2002.
21. S. Nowozin, G. Bakir, and K. Tsuda, "Discriminative Subsequence Mining for Action Classification," in International Conference on Computer Vision, 2007, pp. 1-8.