2026-09-19T08:05:22Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/824152022-01-13T07:54:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Ni, Yuzhao
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology. Computation for Design and Optimization Program
2013-11-18T19:20:12Z
2013-11-18T19:20:12Z
2013
2013
http://hdl.handle.net/1721.1/82415
862813853
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 67-72).
In this thesis, we designed and implemented a crowdsourcing system to annotate mouse behaviors in videos; this involves the development of a novel clip-based video labeling tools, that is more efficient than traditional labeling tools in crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to determine the effectiveness of our labeling tool, inference algorithms and the overall system.
by Yuzhao Ni.
S.M.
72 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Computation for Design and Optimization Program.
Mouse behavior recognition with the wisdom of crowd
Thesis
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<Abstract>In this thesis, we designed and implemented a crowdsourcing system to annotate mouse behaviors in videos; this involves the development of a novel clip-based video labeling tools, that is more efficient than traditional labeling tools in crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to determine the effectiveness of our labeling tool, inference algorithms and the overall system.</Abstract>
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