### Abstract

Original language | English |
---|---|

Title of host publication | Computer Vision - ECCV 2014 Workshops |

Subtitle of host publication | Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I |

Editors | Lourdes Agapito, Michael M. Bronstein, Carsten Rother |

Place of Publication | Zurich |

Publisher | Springer |

Pages | 818-833 |

Number of pages | 16 |

ISBN (Electronic) | 978-3-319-16178-5 |

ISBN (Print) | 978-3-319-16177-8 |

DOIs | |

Publication status | Published - 19 Mar 2015 |

### Publication series

Name | Lecture Notes in Computing Science |
---|---|

Publisher | Springer |

Volume | 8925 |

ISSN (Print) | 0302-9743 |

### Fingerprint

### Keywords

- action recognition
- canonical correlation analysis
- adjacent frame
- human action recognition
- gait recognition

### Cite this

*Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I*(pp. 818-833). (Lecture Notes in Computing Science; Vol. 8925). Zurich: Springer . https://doi.org/10.1007/978-3-319-16178-5_57

**Mode-Driven Volume Analysis Based on Correlation of Time Series.** / Jia, Chengcheng; Pang, Wei; Fu, Yun.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I.*Lecture Notes in Computing Science, vol. 8925, Springer , Zurich, pp. 818-833. https://doi.org/10.1007/978-3-319-16178-5_57

}

TY - GEN

T1 - Mode-Driven Volume Analysis Based on Correlation of Time Series

AU - Jia, Chengcheng

AU - Pang, Wei

AU - Fu, Yun

PY - 2015/3/19

Y1 - 2015/3/19

N2 - Tensor analysis is widely used for face recognition and action recognition. In this paper, a mode-driven discriminant analysis (MDA) in tensor subspace is proposed for visual recognition. For training, we treat each sample as an N-order tensor, of which the first N-1 modes capture the spatial information of images while the N-th mode captures the sequential patterns of images. We employ Fisher criteria on the first N-1 modes to extract discriminative features of the visual information. After that, considering the correlation of adjacent frames in the sequence, i.e., the current frame and its former and latter ones, we update the sequence by calculating the correlation of triple adjacent frames, then perform discriminant analysis on the N-th mode. The alternating projection procedure of MDA converges and is convex with different initial values of the transformation matrices. Such hybrid tensor subspace learning scheme may sufficiently preserve both discrete and continuous distributions information of action videos in lower dimensional spaces to boost discriminant power. Experiments on the MSR action 3D database, KTH database and ETH database showed that our algorithm MDA outperformed other tensor-based methods in terms of accuracy and is competitive considering the time efficiency. Besides, it is robust to deal with the damaged and self-occluded action silhouettes and RGB object images in various viewing angles.

AB - Tensor analysis is widely used for face recognition and action recognition. In this paper, a mode-driven discriminant analysis (MDA) in tensor subspace is proposed for visual recognition. For training, we treat each sample as an N-order tensor, of which the first N-1 modes capture the spatial information of images while the N-th mode captures the sequential patterns of images. We employ Fisher criteria on the first N-1 modes to extract discriminative features of the visual information. After that, considering the correlation of adjacent frames in the sequence, i.e., the current frame and its former and latter ones, we update the sequence by calculating the correlation of triple adjacent frames, then perform discriminant analysis on the N-th mode. The alternating projection procedure of MDA converges and is convex with different initial values of the transformation matrices. Such hybrid tensor subspace learning scheme may sufficiently preserve both discrete and continuous distributions information of action videos in lower dimensional spaces to boost discriminant power. Experiments on the MSR action 3D database, KTH database and ETH database showed that our algorithm MDA outperformed other tensor-based methods in terms of accuracy and is competitive considering the time efficiency. Besides, it is robust to deal with the damaged and self-occluded action silhouettes and RGB object images in various viewing angles.

KW - action recognition

KW - canonical correlation analysis

KW - adjacent frame

KW - human action recognition

KW - gait recognition

U2 - 10.1007/978-3-319-16178-5_57

DO - 10.1007/978-3-319-16178-5_57

M3 - Conference contribution

SN - 978-3-319-16177-8

T3 - Lecture Notes in Computing Science

SP - 818

EP - 833

BT - Computer Vision - ECCV 2014 Workshops

A2 - Agapito, Lourdes

A2 - Bronstein, Michael M.

A2 - Rother, Carsten

PB - Springer

CY - Zurich

ER -