Leveraging Human Brain Activity to Improve Object Classification

DSpace/Manakin Repository

Leveraging Human Brain Activity to Improve Object Classification

Citable link to this page


Title: Leveraging Human Brain Activity to Improve Object Classification
Author: Fong, Ruth Catherine ORCID  0000-0001-8831-6402
Citation: Fong, Ruth Catherine. 2015. Leveraging Human Brain Activity to Improve Object Classification. Bachelor's thesis, Harvard College.
Full Text & Related Files:
Abstract: Today, 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.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:14398538
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search