Learning manifolds of dynamical models for activity recognition

[16] F. Cuzzolin, Manifold learning for multi-dimensional autoregressive
dynamical models, Machine Learning for Visionbased
Motion Analysis (L. Wang, G. Zhao, L. Cheng, and
M. Pietikine, eds.), Springer, 2010.
[17] F. Cuzzolin, D. Mateus, D. Knossow, E. Boyer, and
R. Horaud, Coherent laplacian protrusion segmentation,
CVPR’08.
[18] F. Cuzzolin, A. Sarti, and S. Tubaro, Action modeling with
volumetric data, ICIP’04, vol. 2, pp. 881– 884.
[19] N. Dalai and B. Triggs, Histograms of oriented gradients for
human detection, CVPR’06, pp. 886– 893.
[20] M. N. Do, Fast approximation of Kullback-Leibler distance
for dependence trees and hidden Markov models, IEEE Signal
Processing Letters 10 (2003), no. 4, 115 – 118.
[21] O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce, Automatic
annotation of human actions in video, ICCV’09.
[22] C. F. Eick, A. Rouhana, A. Bagherjeiran, and R. Vilalta, Using
clustering to learn distance functions for supervised similarity
assessment, ICML and Data Mining, 2005.
[23] R. Elliot, L. Aggoun, and J. Moore, Hidden markov models:
estimation and control, Springer Verlag, 1995.
[24] J. M. Wang et. al., Gaussian process dynamical model,
NIPS’05.
[25] L. Ralaivola et. al., Dynamical modeling with kernels for
nonlinear time series prediction, NIPS’04.
[26] A. Galata, N. Johnson, and D. Hogg, Learning variablelength
Markov models of behavior, CVIU 81 (2001), no. 3,
398–413.
[27] A. Gilbert, J. Illingworth, and R. Bowden, Scale invariant
action recognition using compound features mined from
dense spatio-temporal corners, 2008, pp. I: 222–233.
[28] A. Gupta, P. Srinivasan, J. Shi, and L. S. Davis, Understanding
videos, constructing plots learning a visually
grounded storyline model from annotated videos., CVPR’09,
pp. 2012–2019.
[29] D. Han, L. Bo, and C. Sminchisescu, Selection and context
for action recognition, ICCV’09.
[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,
CVPR’09.
[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.
[42] B. North, A. Blake, M. Isard, and J. Rittscher, Learning and
classification of complex dynamics, PAMI 22 (2000), no. 9.
[43] M. Piccardi and O. Perez, Hidden Markov models with kernel
density estimation of emission probabilities and their use in
activity recognition, VS’07, pp. 1–8.
[44] K. Rapantzikos, Y. Avrithis, and S. Kollias, Dense saliencybased
spatiotemporal 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.