Project Proposal (PDF) - Oxford Brookes University

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FP7-ICT-2011-9 STREP proposal 18/01/12 v1 [Dynact] [30] N. Ikizler-Cinbis, R. G. Cinbis, and S. Sclaroff, Learning actions from the web, ICCV’09. [31] M. Itoh and Y. Shishido, Fisher information metric and Poisson kernels, Differential Geometry and its Applications 26 (2008), no. 4, 347 – 356. [32] T. K. Kim and R. Cipolla, Canonical correlation analysis of video volume tensors for action categorization and detection, 31 (2009), no. 8, 1415–1428. [33] S. Kullback and R. A. Leibler, On information and sufficiency, Annals of Math. Stat. 22 (1951), 79–86. [34] G. Lebanon, Metric learning for text documents, IEEE Tr. PAMI 28 (2006), no. 4, 497–508. [35] R. N. Li, R. Chellappa, and S. H. K. Zhou, Learning multimodal densities on discriminative temporal interaction manifold for group activity recognition. [36] Z. Lin, Z. Jiang, and L. S. Davis, Recognizing actions by shape-motion prototype trees, ICCV’09, pp. 444–451. [37] J. G. Liu, J. B. Luo, and M. Shah, Recognizing realistic actions from videos ’in the wild’, CVPR’09, pp. 1996–2003. [38] C. C. Loy, T. Xiang, and S. Gong, Modelling activity global temporal dependencies using time delayed probabilistic graphical model, ICCV’09. [39] M. Marszalek, I. Laptev, and C. Schmid, Actions in context, Proc. of CVPR, pp. 2929-2936, 2009. [40] D. Mateus, R. Horaud, D. Knossow, F. Cuzzolin, and E. Boyer, Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration, CVPR’08. [41] C. Nandini and C. N. Ravi Kumar, Comprehensive framework to gait recognition, Int. J. Biometrics 1 (2008), no. 1, 129–137. [44] K. Rapantzikos, Y. Avrithis, and S. Kollias, Dense saliency-based spatio-temporal feature points for action recognition, CVPR’09, pp. 1454–1461. [45] K. K. Reddy, J. Liu, and M. Shah, Incremental action recognition using feature-tree, ICCV’09. [46] G. Rogez, J. Rihan, S. Ramalingam, C. Orrite, and P. H. S. Torr, Randomized trees for human pose detection, CVPR’08. [47] K. Schindler and L. van Gool, Action snippets: How many frames does human action recognition require?, CVPR’08. [48] M. Schultz and T. Joachims, Learning a distance metric from relative comparisons, NIPS’04. [49] H. J. Seo and P. Milanfar, Detection of human actions from a single example, ICCV’09. [50] N. Shental, T. Hertz, D.Weinshall, and M. Pavel, Adjustment learning and relevant component analysis, ECCV’02. [51] Q. F. Shi, L. Wang, L. Cheng, and A. Smola, Discriminative human action segmentation and recognition using semi-Markov model, CVPR’08. [52] A. J. Smola and S. V. N. Vishwanathan, Hilbert space embeddings in dynamical systems, IFAC’03, pp. 760 – 767. [53] J. Sun, X. Wu, S. C. Yan, L. F. Cheong, T. S. Chua, and J. T. Li, Hierarchical spatio-temporal context modeling for action recognition, CVPR’09, pp. 2004–2011. [54] A. Sundaresan, A. K. Roy Chowdhury, and R. Chellappa, A hidden Markov model based framework for recognition of humans from gait sequences, ICIP’03, pp. II: 93–96. [55] I. W. Tsang, J. T. Kwok, C. W. Bay, and H. Kong, Distance metric learning with kernels, ICAI’03. [56] Y. Wang and G. Mori, Max-margin hidden conditional random fields for human action recognition, CVPR’09, pp. 872–879. [57] E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russel, Distance metric learning with applications to clustering with side information, NIPS’03. [58] B. Yao and S.C Zhu, Learning deformable action templates from cluttered videos, ICCV’09. [59] Y. Hu, L. Cao, F. Lv, S. Yan, Y. Gong, and T. S. Huang, Action detection in complex scenes with spatial and temporal ambiguities, ICCV’09. [60] J. S. Yuan, Z. C. Liu, and Y. Wu, Discriminative subvolume search for efficient action detection, CVPR’09, pp. 2442–2449. [61] Z. Zhang, Learning metrics via discriminant kernels and multidimensional scaling: Toward expected euclidean representation, ICML’03. [62] G. de Cooman, F. Hermans, M. Zaffalon, and A. Antonucci, Epistemic irrelevance in credal nets: the case of imprecise Markov trees, Journal of Approximate Reasoning 51 (2010) 1029-1052. [63] A. Antonucci, A. Benavoli, M. Zaffalon, G. de Cooman, and F. Hermans, Multiple Model Tracking by Imprecise Markov Trees, Fusion pp. 1767-1774. [64] J. De Bock and G. de Cooman, State sequence prediction in imprecise hidden Markov models, ISIPTA 2011 (submitted). Proposal Part B: page [64] of [67]
FP7-ICT-2011-9 STREP proposal 18/01/12 v1 [Dynact] [65] L. M. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE 77 (1989) 257-286. [66] Cozman, F. G., Credal networks, Artificial Intelligence 120 (2000) 199-233. [67] A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm, J. of the Royal Statistical Society B, 39:1–38, 1977. [68] J.M. Wang et. al., Gaussian process dynamical model. In NIPS’06, 18:1441–1448, 2006. [69] L. Ralaivola et. al. Dynamical modeling with kernels for nonlinear time series prediction. In NIPS’03, 16, 2003. [70] I. Laptev and T. Lindeberg. Local descriptors for spatio-temporal recognition. In ICCV’03, 2003. [71] Baraniuk, R.G., Wakin, M.B.: Random projections for manifold learning. In: Proceedings of NIPS. (2007) [72] Baraniuk, R.G., Wakin, M.B.: Random projections of smooth manifolds. Found. Comput. Math. 9 (2009) 51–77 [73] Candes, E., Tao, T.: Near optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inform. Theory 52 (2006) 5406–5425 [74] Baraniuk, R., Davenport, M., DeVore, R.,Wakin, M.: A simple proof of the restricted isometry property for random matrices. Constructive Approximation (2007) [75] Candes, E.: The restricted isometry property and its implications for compressed sensing. Compte Rendus de lAcademie des Sciences (2008) [76] Calderbank, R., Jafarpour, S., Schapire, R.: Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain. Technical report (2009) [77] Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: Proceedings of ICCV’09. (2009) [78] Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 (1998) 1299–1319 [79] Rahimi, A., Recht, B.: Random features for large-scale kernel machines. In: Proceedings of NIPS. (2007) [80] L. de Campos, J. Huete, and S. Moral, Probability intervals: a tool for uncertain reasoning, Int. J. Uncertainty Fuzziness Knowledge-Based Syst., 167–196, 1994 [81] I. Levi, The enterprise of knowledge, MIT Press, 1980 [82] P. Walley, Statistical reasoning with imprecise probabilities, Chapman and Hall, 1991 [83] G. Shafer, A mathematical theory of evidence, Princeton University Press, 1976 [84] A. P. Dempster, Upper and lower probabilities generated by a random closed interval, Annals of Mathematical Statistics, Vol. 39, 957-966, 1968 [85] P. Kohli and Ph. Torr, Efficiently solving dynamic Markov random fields using graph cuts, Proc. of ICCV'05, Vol. 2, pp. 922-929, 2005 [86] E. Miranda and G. de Cooman, Marginal extension in the theory of coherent lower previsions, International Journal of Approximate Reasoning, Vol. 46(1), pp. 188-225, 2007 [87] I. Laptev, M. Marszalek, C. Schmid and B. Rozenfeld, Learning Realistic Human Actions from Movies, Proc. CVPR'08. [88] I. Tsochantaridis, T. Joachims, T. Hofmann and Y. Altun (2005), , JMLR, Vol. 6, pages 1453-1484. [89] G. BakIr, B. Taskar, T. Hofmann, B. Schölkopf, A. Smola and SVN Vishwanathan (2007), , MIT Press. [90] Y.M. Kim, C. Theobalt, J. Diebel, J. Kosecka, B. Miscusik, S. Thrun, Multi-view Image and ToF Sensor Fusion for Dense 3D Reconstruction, Proc. of 3DIM 2009. [91] Toth, C., N. Markiel and D. Brzezinska, An Algorithm for Automated Feature Extraction from Laser Ranging Data, ASPRS PECORA Conference, Denver, CO, November 19 – 21, 2008 [92] A. Kolb, E. Barth, R. Koch and R. Larsen, Time-of-flight cameras in computer graphics, Computer Graphics Forum, Vol. 29(1), pages 141-159, 2010. [93] Y.-W. Chee, T.-L. Yang, Vision and LIDAR Feature Extraction, Tech report, Cornell University [94] M.C.M. Troffaes, Decision making under uncertainty using imprecise probabilities, International Journal of Approximate Reasoning 45 (2007) 17-29. [95] G. de Cooman and M. Zaffalon, Updating beliefs with incomplete observations, Artificial Intelligence 159 (2004) 75-125. [96] M. Zaffalon and E. Miranda, Conservative Inference Rule for Uncertain Reasoning under Incompleteness, Journal Of Artificial Intelligence Research 34 (2009) 757-821. [97] G. de Cooman and E. Miranda, Forward irrelevance, Journal Of Statistical Planning And Inference 139 (2009) 256-276. Proposal Part B: page [65] of [67]
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