IEICE TRANSACTIONS on Information
Action Recognition Using Visual-Neuron Feature
Summary :
This letter proposes a neurobiological approach for action recognition. In this approach, actions are represented by a visual-neuron feature (VNF) based on a quantitative model of object representation in the primate visual cortex. A supervised classification technique is then used to classify the actions. The proposed VNF is invariant to affine translation and scaling of moving objects while maintaining action specificity. Moreover, it is robust to the deformation of actors. Experiments on publicly available action datasets demonstrate the proposed approach outperforms conventional action recognition models based on computer-vision features.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.2 pp.361-364
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.361
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Ning LI, De XU, "Action Recognition Using Visual-Neuron Feature" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 2, pp. 361-364, February 2009, doi: 10.1587/transinf.E92.D.361.
Abstract: This letter proposes a neurobiological approach for action recognition. In this approach, actions are represented by a visual-neuron feature (VNF) based on a quantitative model of object representation in the primate visual cortex. A supervised classification technique is then used to classify the actions. The proposed VNF is invariant to affine translation and scaling of moving objects while maintaining action specificity. Moreover, it is robust to the deformation of actors. Experiments on publicly available action datasets demonstrate the proposed approach outperforms conventional action recognition models based on computer-vision features.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.361/_p
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@ARTICLE{e92-d_2_361,
author={Ning LI, De XU, },
journal={IEICE TRANSACTIONS on Information},
title={Action Recognition Using Visual-Neuron Feature},
year={2009},
volume={E92-D},
number={2},
pages={361-364},
abstract={This letter proposes a neurobiological approach for action recognition. In this approach, actions are represented by a visual-neuron feature (VNF) based on a quantitative model of object representation in the primate visual cortex. A supervised classification technique is then used to classify the actions. The proposed VNF is invariant to affine translation and scaling of moving objects while maintaining action specificity. Moreover, it is robust to the deformation of actors. Experiments on publicly available action datasets demonstrate the proposed approach outperforms conventional action recognition models based on computer-vision features.},
keywords={},
doi={10.1587/transinf.E92.D.361},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Action Recognition Using Visual-Neuron Feature
T2 - IEICE TRANSACTIONS on Information
SP - 361
EP - 364
AU - Ning LI
AU - De XU
PY - 2009
DO - 10.1587/transinf.E92.D.361
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2009
AB - This letter proposes a neurobiological approach for action recognition. In this approach, actions are represented by a visual-neuron feature (VNF) based on a quantitative model of object representation in the primate visual cortex. A supervised classification technique is then used to classify the actions. The proposed VNF is invariant to affine translation and scaling of moving objects while maintaining action specificity. Moreover, it is robust to the deformation of actors. Experiments on publicly available action datasets demonstrate the proposed approach outperforms conventional action recognition models based on computer-vision features.
ER -
English
