Mouse Behavior Recognition with The Wisdom of Crowd
Author(s)Ni, Yuzhao; Frogner, Charles A.; Poggio, Tomaso A.
Center for Biological and Computational Learning (CBCL)
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In this thesis, we designed and implemented a crowdsourcing system to annotatemouse 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.
crowdsourcing, video labeling, human computation, mouse phenotyping, action recognition