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 spatiotemporal
interest points, leading to enhanced performance, (ii) we encode contextual features and then fuse
them with appearance features, and (iii) 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. [7] and the results show that our method outperforms several state-of-the-art approaches.
Original language | English |
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Title of host publication | Proceedings of the International Conference on Pattern Recognition Applications and Methods (ICPRAM) |
Publisher | SciTePress |
Publication status | Published - 26 Feb 2017 |
Event | International Conference on Pattern Recognition Applications and Methods (ICPRAM) - Porto, Portugal Duration: 24 Feb 2017 → 26 Feb 2017 http://www.icpram.org/ |
Conference
Conference | International Conference on Pattern Recognition Applications and Methods (ICPRAM) |
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Country | Portugal |
City | Porto |
Period | 24/02/2017 → 26/02/2017 |
Internet address |