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Authors: Zheheng Jiang 1 ; Danny Crookes 1 ; Brian Desmond Green 1 ; Shengping Zhang 2 and Huiyu Zhou 1

Affiliations: 1 Queen's University Belfast, United Kingdom ; 2 Harbin Institute of Tehcnology, China

ISBN: 978-989-758-222-6

Keyword(s): Mouse Behavior Recognition, Spatial-temporal Stacked Fisher Vector, Gaussian Mixture Model, Contextual Features, Spatio-temporal Interest Points.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Image Understanding ; Learning of Action Patterns ; Pattern Recognition ; Software Engineering ; Video Analysis

Abstract: Manual measurement of mouse behavior is highly labor intensive and prone to error. This investigation aims to efficiently and accurately recognize individual mouse behaviors in action videos and continuous videos. In our system each mouse action video is expressed as the collection of a set of interest points. We extract both appearance and contextual features from the interest points collected from the training datasets, and then obtain two Gaussian Mixture Model (GMM) dictionaries for the visual and contextual features. The two GMM dictionaries are leveraged by our spatial-temporal stacked Fisher Vector (FV) to represent each mouse action video. A neural network is used to classify mouse action and finally applied to annotate continuous video. The novelty of our proposed approach is: (i) our method exploits contextual features from spatio-temporal interest points, leading to enhanced performance, (ii) we encode contextual features and then fuse them with appearance features, and (ii i) location information of a mouse is extracted from spatio-temporal interest points to support mouse behavior recognition. We evaluate our method against the database of Jhuang et al. (Jhuang et al., 2010) and the results show that our method outperforms several state-of-the-art approaches. (More)

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Paper citation in several formats:
Jiang, Z.; Crookes, D.; Green, B.; Zhang, S. and Zhou, H. (2017). Behavior Recognition in Mouse Videos using Contextual Features Encoded by Spatial-temporal Stacked Fisher Vectors.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 259-269. DOI: 10.5220/0006244602590269

@conference{icpram17,
author={Zheheng Jiang. and Danny Crookes. and Brian Desmond Green. and Shengping Zhang. and Huiyu Zhou.},
title={Behavior Recognition in Mouse Videos using Contextual Features Encoded by Spatial-temporal Stacked Fisher Vectors},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={259-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006244602590269},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Behavior Recognition in Mouse Videos using Contextual Features Encoded by Spatial-temporal Stacked Fisher Vectors
SN - 978-989-758-222-6
AU - Jiang, Z.
AU - Crookes, D.
AU - Green, B.
AU - Zhang, S.
AU - Zhou, H.
PY - 2017
SP - 259
EP - 269
DO - 10.5220/0006244602590269

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