Abstract:
Video and image classification based on Instance-to-Class (I2C) distance attracted many recent studies, due to the good generalization capabilities it provides for non-parametric classifiers. In this work we propose a method for action recognition. Our approach needs no intensive learning stage, and its classification performance is comparable to the state-of-the-art. A smart organization of training data allows the classifier to achieve reasonable computation times when working with large training databases. An efficient method for organizing training data in such a way is proposed. We perform thorough experiments on two popular action recognition datasets: the KTH dataset and the IXMAS dataset, and we study the influence of one of the key parameters of the method on classification performance. © 2013 Elsevier B.V. All rights reserved.
Registro:
Documento: |
Artículo
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Título: | Efficient descriptor tree growing for fast action recognition |
Autor: | Ubalde, S.; Goussies, N.A.; Mejail, M.E. |
Filiación: | Departamento de Computation, Facultad de Ciencias Exactes y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
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Palabras clave: | Action recognition; Instance-to-Class distance; Nearest neighbor; Pattern recognition; Software engineering; Action recognition; Class-distance; Classification performance; Generalization capability; Nearest neighbors; Non-parametric classifiers; State of the art; Training database; Classification (of information) |
Año: | 2014
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Volumen: | 36
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Número: | 1
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Página de inicio: | 213
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Página de fin: | 220
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DOI: |
http://dx.doi.org/10.1016/j.patrec.2013.05.007 |
Título revista: | Pattern Recognition Letters
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Título revista abreviado: | Pattern Recogn. Lett.
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ISSN: | 01678655
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Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v36_n1_p213_Ubalde |
Referencias:
- Bay, H., Tuytelaars, T., Van Gool, L., SURF: Speeded up robust features (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3951, pp. 404-417. , DOI 10.1007/11744023-32, Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
- Boiman, O., Shechtman, E., Irani, M., In defense of nearest-neighbor based image classification (2008) CVPR08, pp. 1-8
- Chómát, O., Martin, J., Crowley, J., A probabilistic sensor for the perception and the recognition of activities (2000) ECCVOO, 1, pp. 487-503
- Davis, J., Bobick, A., The representation and recognition of action using temporal templates (1997) CVPR97, pp. 928-934
- Dollar, P., Rabaud, V., Cottrell, G., Belongie, S., Behavior recognition via sparse spatio-temporal features (2005) Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, 2005, pp. 65-72. , DOI 10.1109/VSPETS.2005.1570899, 1570899, Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS
- Grundmann, M., Meier, F., Essa, I., 3d shape context and distance transform for action recognition (2008) ICPRO, pp. 1-4
- Harris, C., Stephens, M., A combined corner and edge detector (1988) Alvey88, pp. 147-152
- Laptev, I., On space-time interest points (2005) International Journal of Computer Vision, 64 (2-3), pp. 107-123. , DOI 10.1007/s11263-005-1838-7
- Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B., Learning realistic human actions from movies (2008) CVPR08, pp. 1-8
- Liu, J., Shah, M., Learning human actions via information maximization (2008) CVPR08, pp. 1-8
- Muja, M., Lowe, D.G., Fast approximate nearest neighbors with automatic algorithm configuration (2009) International Conference on Computer Vision Theory and Application VISSAPP'09), pp. 331-340. , INSTICC Press
- Niebles, J., Wang, H., Fei Fei, L., Unsupervised learning of human action categories using spatial-temporal words (2006) BMVC06, 3, p. 1249
- Nowozin, S., Bakir, G., Tsuda, K., Discriminative subsequence mining for action classification (2007) ICCV07, pp. 1-8
- Rapantzikos, K., Avrithis, Y., Kollias, S., Spatiotemporal saliency for event detection and representation in the 3D wavelet domain: Potential in human action recognition (2007) Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007, pp. 294-301. , DOI 10.1145/1282280.1282326, Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007
- Schuldt, C., Laptev, I., Caputo, B., Recognizing human actions: A local svm approach (2004) ICPR04, 3, pp. 32-36
- Sivic, J., Zisserman, A., Video google: A text retrieval approach to object matching in videos (2003) ICCV03, pp. 1470-1477
- Souvenir, R., Babbs, J., Learning the viewpoint manifold for action recognition (2008) CVPR08, pp. 1-7
- Ubalde, S., Goussies, N., Fast non-parametric action recognition (2012) CIARP, pp. 268-275
- Liang, W., Suter, D., Informative shape representations for human action recognition (2006) Proceedings - International Conference on Pattern Recognition, 2, pp. 1266-1269. , DOI 10.1109/ICPR.2006.711, 1699440, Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006
- Wang, Z., Hu, Y., Chia, L., Learning instance-to-class distance for human action recognition (2009) ICIP09, pp. 3545-3548
- Weinland, D., Boyer, E., Ronfard, R., Action recognition from arbitrary views using 3d exemplars (2007) ICCV07, pp. 1-7
- Willems, G., Tuytelaars, T., Van Gooi, L., An efficient dense and scale-invariant spatio-temporal interest point detector (2008) ECCV08, 2, pp. 650-663
- Yan, P., Khan, S., Shah, M., Learning 4d action feature models for arbitrary view action recognition (2008) CVPR08, pp. 1-7
- Yilmaz, A., Shah, M., A differential geometric approach to representing the human actions (2008) Computer Vision and Image Understanding, 109 (3), pp. 335-351. , DOI 10.1016/j.cviu.2007.09.006, PII S1077314207001397
- Yuan, J., Liu, Z., Wu, Y., Discriminative subvolume search for efficient action detection (2009) CVPR09, pp. 2442-2449
- Yuan, J., Liu, Z., Wu, Y., Discriminative video pattern search for efficient action detection (2011) PAMI, 33 (9), pp. 1728-1743
- Zelnikmanor, L., Irani, M., Event-based analysis of video (2001) Proc. CVPR, pp. 123-130
- Weinland, D., Ronfard, R., Boyer, E., Free viewpoint action recognition using motion history volumes (2006) Computer Vision and Image Understanding, 104 (2-3 SPEC. ISS.), pp. 249-257. , DOI 10.1016/j.cviu.2006.07.013, PII S1077314206001081
- Zhu, P., Hu, W., Li, L., Wei, Q., Human activity recognition based on r transform and fourier mellin transform (2009) ISVC09, 2, pp. 631-640
Citas:
---------- APA ----------
Ubalde, S., Goussies, N.A. & Mejail, M.E.
(2014)
. Efficient descriptor tree growing for fast action recognition. Pattern Recognition Letters, 36(1), 213-220.
http://dx.doi.org/10.1016/j.patrec.2013.05.007---------- CHICAGO ----------
Ubalde, S., Goussies, N.A., Mejail, M.E.
"Efficient descriptor tree growing for fast action recognition"
. Pattern Recognition Letters 36, no. 1
(2014) : 213-220.
http://dx.doi.org/10.1016/j.patrec.2013.05.007---------- MLA ----------
Ubalde, S., Goussies, N.A., Mejail, M.E.
"Efficient descriptor tree growing for fast action recognition"
. Pattern Recognition Letters, vol. 36, no. 1, 2014, pp. 213-220.
http://dx.doi.org/10.1016/j.patrec.2013.05.007---------- VANCOUVER ----------
Ubalde, S., Goussies, N.A., Mejail, M.E. Efficient descriptor tree growing for fast action recognition. Pattern Recogn. Lett. 2014;36(1):213-220.
http://dx.doi.org/10.1016/j.patrec.2013.05.007