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
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 (i
ii) 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.
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