Project Proposal (PDF) - Oxford Brookes University
Project Proposal (PDF) - Oxford Brookes University
Project Proposal (PDF) - Oxford Brookes University
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FP7-ICT-2011-9 STREP proposal<br />
18/01/12 v1 [Dynact]<br />
[30] N. Ikizler-Cinbis, R. G. Cinbis, and S. Sclaroff, Learning actions from the web, ICCV’09.<br />
[31] M. Itoh and Y. Shishido, Fisher information metric and Poisson kernels, Differential Geometry and its<br />
Applications 26 (2008), no. 4, 347 – 356.<br />
[32] T. K. Kim and R. Cipolla, Canonical correlation analysis of video volume tensors for action<br />
categorization and detection, 31 (2009), no. 8, 1415–1428.<br />
[33] S. Kullback and R. A. Leibler, On information and sufficiency, Annals of Math. Stat. 22 (1951), 79–86.<br />
[34] G. Lebanon, Metric learning for text documents, IEEE Tr. PAMI 28 (2006), no. 4, 497–508.<br />
[35] R. N. Li, R. Chellappa, and S. H. K. Zhou, Learning multimodal densities on discriminative temporal<br />
interaction manifold for group activity recognition.<br />
[36] Z. Lin, Z. Jiang, and L. S. Davis, Recognizing actions by shape-motion prototype trees, ICCV’09, pp.<br />
444–451.<br />
[37] J. G. Liu, J. B. Luo, and M. Shah, Recognizing realistic actions from videos ’in the wild’, CVPR’09, pp.<br />
1996–2003.<br />
[38] C. C. Loy, T. Xiang, and S. Gong, Modelling activity global temporal dependencies using time delayed<br />
probabilistic graphical model, ICCV’09.<br />
[39] M. Marszalek, I. Laptev, and C. Schmid, Actions in context, Proc. of CVPR, pp. 2929-2936, 2009.<br />
[40] D. Mateus, R. Horaud, D. Knossow, F. Cuzzolin, and E. Boyer, Articulated shape matching using<br />
Laplacian eigenfunctions and unsupervised point registration, CVPR’08.<br />
[41] C. Nandini and C. N. Ravi Kumar, Comprehensive framework to gait recognition, Int. J. Biometrics 1<br />
(2008), no. 1, 129–137.<br />
[44] K. Rapantzikos, Y. Avrithis, and S. Kollias, Dense saliency-based spatio-temporal feature points for<br />
action recognition, CVPR’09, pp. 1454–1461.<br />
[45] K. K. Reddy, J. Liu, and M. Shah, Incremental action recognition using feature-tree, ICCV’09.<br />
[46] G. Rogez, J. Rihan, S. Ramalingam, C. Orrite, and P. H. S. Torr, Randomized trees for human pose<br />
detection, CVPR’08.<br />
[47] K. Schindler and L. van Gool, Action snippets: How many frames does human action recognition<br />
require?, CVPR’08.<br />
[48] M. Schultz and T. Joachims, Learning a distance metric from relative comparisons, NIPS’04.<br />
[49] H. J. Seo and P. Milanfar, Detection of human actions from a single example, ICCV’09.<br />
[50] N. Shental, T. Hertz, D.Weinshall, and M. Pavel, Adjustment learning and relevant component analysis,<br />
ECCV’02.<br />
[51] Q. F. Shi, L. Wang, L. Cheng, and A. Smola, Discriminative human action segmentation and<br />
recognition using semi-Markov model, CVPR’08.<br />
[52] A. J. Smola and S. V. N. Vishwanathan, Hilbert space embeddings in dynamical systems, IFAC’03, pp.<br />
760 – 767.<br />
[53] J. Sun, X. Wu, S. C. Yan, L. F. Cheong, T. S. Chua, and J. T. Li, Hierarchical spatio-temporal context<br />
modeling for action recognition, CVPR’09, pp. 2004–2011.<br />
[54] A. Sundaresan, A. K. Roy Chowdhury, and R. Chellappa, A hidden Markov model based framework for<br />
recognition of humans from gait sequences, ICIP’03, pp. II: 93–96.<br />
[55] I. W. Tsang, J. T. Kwok, C. W. Bay, and H. Kong, Distance metric learning with kernels, ICAI’03.<br />
[56] Y. Wang and G. Mori, Max-margin hidden conditional random fields for human action recognition,<br />
CVPR’09, pp. 872–879.<br />
[57] E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russel, Distance metric learning with applications to<br />
clustering with side information, NIPS’03.<br />
[58] B. Yao and S.C Zhu, Learning deformable action templates from cluttered videos, ICCV’09.<br />
[59] Y. Hu, L. Cao, F. Lv, S. Yan, Y. Gong, and T. S. Huang, Action detection in complex scenes with<br />
spatial and temporal ambiguities, ICCV’09.<br />
[60] J. S. Yuan, Z. C. Liu, and Y. Wu, Discriminative subvolume search for efficient action detection,<br />
CVPR’09, pp. 2442–2449.<br />
[61] Z. Zhang, Learning metrics via discriminant kernels and multidimensional scaling: Toward expected<br />
euclidean representation, ICML’03.<br />
[62] G. de Cooman, F. Hermans, M. Zaffalon, and A. Antonucci, Epistemic irrelevance in credal nets: the<br />
case of imprecise Markov trees, Journal of Approximate Reasoning 51 (2010) 1029-1052.<br />
[63] A. Antonucci, A. Benavoli, M. Zaffalon, G. de Cooman, and F. Hermans, Multiple Model Tracking by<br />
Imprecise Markov Trees, Fusion pp. 1767-1774.<br />
[64] J. De Bock and G. de Cooman, State sequence prediction in imprecise hidden Markov models, ISIPTA<br />
2011 (submitted).<br />
<strong>Proposal</strong> Part B: page [64] of [67]