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You searched for subject:(behavior pattern annotation). Showing records 1 – 2 of 2 total matches.

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University of Southern California

1. Meng, Ye. Animal behavior pattern annotation and performance evaluation.

Degree: MS, Biostatistics, 2014, University of Southern California

Learning animal behavior patterns, such as the social interactions of flies, is of great interest both in terms of improving our understanding of how those patterns emerge and as a precursor for looking for genetic determinants of those behaviors. Manual annotation is labor‐intensive, so automated techniques are much needed. A popular tool for semi‐automatic annotation of fly behavior, based upon video tracking data, is the JAABA software package. In this thesis we propose methods based on machine learning techniques to refine the analysis of sample data from a JAABA document. Machine learning algorithms are applied to behavior outcome classification, since they are ideal for large datasets. We do this using a self‐written Python package. In our case, flies are characterized via approximately 50 per‐frame features. Those features capture relevant aspects of the tracking data. While no feature can predict behavior in itself, combinations of features are able to do so. We demonstrate how to do so, thereby offering scope for improving the accuracy as well as speed of classification in future. Advisors/Committee Members: Marjoram, Paul (Committee Chair), Azen, Stanley P. (Committee Member), Nuzhdin, Sergey V. (Committee Member).

Subjects/Keywords: fly; behavior pattern annotation; machine learning; cross‐validation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Meng, Y. (2014). Animal behavior pattern annotation and performance evaluation. (Masters Thesis). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/385363/rec/828

Chicago Manual of Style (16th Edition):

Meng, Ye. “Animal behavior pattern annotation and performance evaluation.” 2014. Masters Thesis, University of Southern California. Accessed September 22, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/385363/rec/828.

MLA Handbook (7th Edition):

Meng, Ye. “Animal behavior pattern annotation and performance evaluation.” 2014. Web. 22 Sep 2019.

Vancouver:

Meng Y. Animal behavior pattern annotation and performance evaluation. [Internet] [Masters thesis]. University of Southern California; 2014. [cited 2019 Sep 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/385363/rec/828.

Council of Science Editors:

Meng Y. Animal behavior pattern annotation and performance evaluation. [Masters Thesis]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/385363/rec/828

2. Lorbach, M.T.|info:eu-repo/dai/nl/380611384. Automated Recognition of Rodent Social Behavior.

Degree: 2017, University Utrecht

Social behavior is an important aspect of rodent models in behavioral neuroscience. Abnormal social behavior can indicate the onset of conditions such as Huntington’s disease. Studying social behavior requires to objectively quantify the occurrence of specific rodent interactions. While this can be done manually by annotating occurrences in videos, manual annotation is a time-consuming and sometimes subjective process. Human observers need five to ten times as long as the length of the video. We therefore aim to reduce the manual effort by automating the annotation process. Automated annotation involves a computational model that distinguishes between the different behaviors using visual information from the video such as the relative motion and pose of the rodents. Before the model can be applied, it is trained with labeled examples of every behavior. Rodent social behavior classification is a challenging task. The classification method has to deal with highly unbalanced occurrence rates of the different behaviors, causing less frequent behaviors to be underrepresented. Furthermore, behavior categories sometimes leave room for interpretation which causes even human observers to disagree on specific occurrences. Similarly, the precise temporal extent of interactions is often ambiguous. Finally, tracking multiple, visually similar rodents is a demanding task, in particular during close-contact interactions where occlusion is frequent. We find that limited tracking quality inhibits the recognition of close-contact interactions. Once a classification model is trained for a set of interactions, it can be applied to novel videos recorded in the same environment. We demonstrate that it can be difficult to comply to the requirements of a constant environment because they include not only controllable, external factors such as illumination and cage size, but also variations in the tested animal population. In a cross-dataset experiment we use juvenile and adult rats to show that behavior variations due to age can reduce recognition accuracy. We argue for adequate cross-dataset validation and more research into adaptation methods to deal with such variations systematically. If no previous classification model is available, for example because behavior categories are changed or added, the human observer is left with manual annotation. We aim to reduce the effort in such scenarios by formulating the annotation task as an interactive labeling problem. The human starts annotating examples of interactions while the classifier learns to distinguish them. Once the classifier has learned sufficiently, it may take over the annotation and alleviate the user from much of the work. To reduce the time further, we experiment with different strategies that guide the user to annotate particularly useful interaction examples. We demonstrate that placing the human in the annotation loop reduces the annotation time substantially compared to traditional, sequential labeling. Participants in a user study trained an accurate classifier in less… Advisors/Committee Members: Veltkamp, Remco, Poppe, Ronald.

Subjects/Keywords: automated behavior recognition; pattern recognition; social behavior; interactive annotation; rodent behavior; machine learning; video-based behavior recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lorbach, M. T. |. (2017). Automated Recognition of Rodent Social Behavior. (Doctoral Dissertation). University Utrecht. Retrieved from http://dspace.library.uu.nl/handle/1874/356090 ; URN:NBN:NL:UI:10-1874-356090 ; urn:isbn:978-90-393-6872-5 ; URN:NBN:NL:UI:10-1874-356090 ; http://dspace.library.uu.nl/handle/1874/356090

Chicago Manual of Style (16th Edition):

Lorbach, M T |info:eu-repo/dai/nl/380611384. “Automated Recognition of Rodent Social Behavior.” 2017. Doctoral Dissertation, University Utrecht. Accessed September 22, 2019. http://dspace.library.uu.nl/handle/1874/356090 ; URN:NBN:NL:UI:10-1874-356090 ; urn:isbn:978-90-393-6872-5 ; URN:NBN:NL:UI:10-1874-356090 ; http://dspace.library.uu.nl/handle/1874/356090.

MLA Handbook (7th Edition):

Lorbach, M T |info:eu-repo/dai/nl/380611384. “Automated Recognition of Rodent Social Behavior.” 2017. Web. 22 Sep 2019.

Vancouver:

Lorbach MT|. Automated Recognition of Rodent Social Behavior. [Internet] [Doctoral dissertation]. University Utrecht; 2017. [cited 2019 Sep 22]. Available from: http://dspace.library.uu.nl/handle/1874/356090 ; URN:NBN:NL:UI:10-1874-356090 ; urn:isbn:978-90-393-6872-5 ; URN:NBN:NL:UI:10-1874-356090 ; http://dspace.library.uu.nl/handle/1874/356090.

Council of Science Editors:

Lorbach MT|. Automated Recognition of Rodent Social Behavior. [Doctoral Dissertation]. University Utrecht; 2017. Available from: http://dspace.library.uu.nl/handle/1874/356090 ; URN:NBN:NL:UI:10-1874-356090 ; urn:isbn:978-90-393-6872-5 ; URN:NBN:NL:UI:10-1874-356090 ; http://dspace.library.uu.nl/handle/1874/356090

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