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Table 6: Average accuracy for MBPs on the Weizman
dataset in comparison to single- and multi-feature methods.
Name Accuracy (%)
(Jhuang et al., 2007) 98.80
(Lin et al., 2009) 100.00
(Blank et al., 2005) 100.00
(Gorelick et al., 2007) 100.00
Proposed method 100.00
(Schindler and Van Gool, 2008) 100.00
we suggest to encode more information into the pat-
tern. For instance, all temporal shifted patterns could
be integrated into one final histogram. Additionally,
more research is needed to choose the optimal cell
size of a Motion Binary Pattern. In this paper we sug-
gest to compute a MBP in a 3 × 3 cell but the results
might be improved by taking other cell sizes like 5×5
or 7 × 7 .
REFERENCES
Aggarwal, J. and Ryoo, M. (2011). Human activity analy-
sis: A review. ACM Computing Surveys, 43(3):16:1–
16:43.
Amit, Y. and Geman, D. (1997). Shape quantization and
recognition with randomized trees. Neural computa-
tion, 9(7):1545–1588.
Blank, M., Gorelick, L., Shechtman, E., Irani, M., and
Basri, R. (2005). Actions as space-time shapes. In
Computer Vision (ICCV), 10th International Confer-
ence on, pages 1395–1402.
Breiman, L. (1996). Bagging predictors. In Machine Learn-
ing, volume 24, pages 123–140.
Breiman, L. (2001). Random forests. Machine learning,
45(1):5–32.
Fehr, J. (2007). Rotational invariant uniform local binary
patterns for full 3d volume texture analysis. In Finnish
signal processing symposium (FINSIG).
Gorelick, L., Blank, M., Shechtman, E., Irani, M., and
Basri, R. (2007). Actions as space-time shapes. Pat-
tern Analysis and Machine Intelligence (PAMI), IEEE
Transactions on, 29(12):2247–2253.
Ho, T. K. (1995). Random decision forests. In Document
Analysis and Recognition, 1995., Proceedings of the
Third International Conference on, volume 1, pages
278–282. IEEE.
Ho, T. K. (1998). The random subspace method for con-
structing decision forests. Pattern Analysis and Ma-
chine Intelligence, IEEE Transactions on, 20(8):832–
844.
Horn, B. K. and Schunck, B. G. (1981). Determining optical
flow. Artificial Intelligence, 17.
Jhuang, H., Serre, T., Wolf, L., and Poggio, T. (2007). A
biologically inspired system for action recognition. In
Computer Vision (ICCV), 11th International Confer-
ence on, pages 1–8. IEEE.
Kihl, O., Picard, D., Gosselin, P.-H., et al. (2013). Local
polynomial space-time descriptors for actions classi-
fication. In International Conference on Machine Vi-
sion Applications.
Laptev, I., Marszalek, M., Schmid, C., and Rozenfeld,
B. (2008). Learning realistic human actions from
movies. In Computer Vision and Pattern Recognition,
(CVPR). IEEE Conference on.
Li, R. and Zickler, T. (2012). Discriminative virtual views
for cross-view action recognition. In Computer Vision
and Pattern Recognition, (CVPR). IEEE Conference
on.
Li, W., Yu, Q., Sawhney, H., and Vasconcelos, N. (2013).
Recognizing activities via bag of words for attribute
dynamics. In Computer Vision and Pattern Recogni-
tion, (CVPR). IEEE Conference on, pages 2587–2594.
Lin, Z., Jiang, Z., and Davis, L. S. (2009). Recognizing
actions by shape-motion prototype trees. In Com-
puter Vision (ICCV), 12th International Conference
on, pages 444–451. IEEE.
Liu, C. and Yuen, P. C. (2010). Human action recognition
using boosted eigenactions. Image and vision comput-
ing, 28(5):825–835.
Liu, J., Luo, J., and Shah, M. (2009). Recognizing realis-
tic actions from videos “in the wild”. In Computer
Vision and Pattern Recognition, 2009. CVPR 2009.
IEEE Conference on, pages 1996–2003. IEEE.
Lui, Y. M., Beveridge, J., and Kirby, M. (2010). Action clas-
sification on product manifolds. In Computer Vision
and Pattern Recognition, (CVPR). IEEE Conference
on.
Marszałek, M., Laptev, I., and Schmid, C. (2009). Actions
in context. In Computer Vision and Pattern Recogni-
tion, (CVPR). IEEE Conference on.
Mattivi, R. and Shao, L. (2009). Human action recogni-
tion using lbp-top as sparse spatio-temporal feature
descriptor. In Computer Analysis of Images and Pat-
terns (CAIP).
O’Hara, S. and Draper, B. (2012). Scalable action recogni-
tion with a subspace forest. In Computer Vision and
Pattern Recognition, (CVPR). IEEE Conference on.
Ojala, T., Pietikainen, M., and Harwood, D. (1994). Perfor-
mance evaluation of texture measures with classifica-
tion based on kullback discrimination of distributions.
In Pattern Recognition. Proceedings of the 12th IAPR
International Conference on.
Poppe, R. (2010). A survey on vision-based human action
recognition. Image and Vision Computing, 28(6):976
– 990.
Schindler, K. and Van Gool, L. (2008). Action snippets:
How many frames does human action recognition re-
quire? In Computer Vision and Pattern Recognition,
(CVPR). IEEE Conference on.
Schuldt, C., Laptev, I., and Caputo, B. (2004). Recogniz-
ing human actions: a local svm approach. In Pattern
Recognition. (ICPR). Proceedings of the 17th Interna-
tional Conference on.
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