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dc.contributor.authorFong, Ruth Catherineen_US
dc.date.accessioned2015-04-09T13:56:01Z
dc.date.created2015-05en_US
dc.date.issued2015-04-08en_US
dc.date.submitted2015en_US
dc.identifier.citationFong, Ruth Catherine. 2015. Leveraging Human Brain Activity to Improve Object Classification. Bachelor's thesis, Harvard College.en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:14398538
dc.description.abstractToday, most object detection algorithms differ drastically from how humans tackle visual problems. In this thesis, I present a new paradigm for improving machine vision algorithms by designing them to better mimic how humans approach these tasks. Specifically, I demonstrate how human brain activity from functional magnetic resonance imaging (fMRI) can be leveraged to improve object classification. Inspired by the graduated manner in which humans learn, I present a novel algorithm that simulates learning in a similar fashion by more aggressively penalizing the misclassification of certain training datum. I propose a method to learn annotations that capture the difficulty of detecting an object in an image from auxilliary brain activity data. I then demonstrate how to leverage these annotations by using a modified definition of Support Vector Machines (SVMs) that uses these annotations to weight training data in an object classification task. An experimental comparison between my procedure and a parallel control shows that my techniques provide significant improvements in object classification. In particular, my protocol empirically halved the gap in classification accuracy between SVM classifiers that used state-of-the-art, yet computationally intensive convolutional neural net (CNN) features and those that used out-of-the box, efficient histogram of oriented gradients (HOG) descriptors. Further analysis demonstrates that my experimental results support findings in neuroimaging literature about the roles different cortical regions play in object recognition.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dash.licenseLAAen_US
dc.subjectComputer Scienceen_US
dc.subjectBiology, Neuroscienceen_US
dc.titleLeveraging Human Brain Activity to Improve Object Classificationen_US
dc.typeThesis or Dissertationen_US
dash.depositing.authorFong, Ruth Catherineen_US
dc.date.available2015-04-09T13:56:01Z
thesis.degree.date2015en_US
thesis.degree.grantorHarvard Collegeen_US
thesis.degree.levelUndergraduateen_US
thesis.degree.nameABen_US
dc.type.materialtexten_US
thesis.degree.departmentComputer Scienceen_US
dash.identifier.vireohttp://etds.lib.harvard.edu/college/admin/view/60en_US
dash.title.page1en_US
dash.author.emailruthcfong@gmail.comen_US
dash.identifier.drsurn-3:HUL.DRS.OBJECT:25267809en_US
dash.identifier.orcid0000-0001-8831-6402en_US
dash.contributor.affiliatedFong, Ruth
dc.identifier.orcid0000-0001-8831-6402


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