Behavior recognition in mouse videos using contextual features encoded by spatial-temporal stacked fisher vectors

Zheheng Jiang, Danny Crookes, Brian Desmond Green, Shengping Zhang, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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. (Jhuang et al., 2010) and the results show that our method outperforms several state-of-the-art approaches.

Original languageEnglish
Title of host publicationICPRAM 2017: Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
PublisherSciTePress
Pages259-269
Number of pages11
Volume2017-January
ISBN (Electronic)9789897582226
Publication statusPublished - 2017
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: 24 Feb 201726 Feb 2017

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
CountryPortugal
CityPorto
Period24/02/201726/02/2017

Keywords

  • Contextual features
  • Gaussian mixture model
  • Mouse behavior recognition
  • Spatial-Temporal stacked fisher vector
  • Spatio-Temporal interest points

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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