Image captioning algorithms for images taken by people with visual impairments

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dc.contributor.advisorGurari, Danna
dc.creatorZhang, Meng, M.S. in Information Studies
dc.creator.orcid0000-0003-4505-6983
dc.date.accessioned2020-05-07T18:49:24Z
dc.date.available2020-05-07T18:49:24Z
dc.date.created2019-05
dc.date.issued2019-07-08
dc.date.submittedMay 2019
dc.date.updated2020-05-07T18:49:25Z
dc.description.abstractPeople with visual impairments regularly encounter the challenge that their visual impairments expose them to a time-consuming, or even impossible, task: what content is presented in an image without assistance. One method to address this problem is image captioning with machine learning. With the help of image captioning algorithms together with artificial intelligence speech system, people who are blind can instantly learn what is in an image, since such systems can automatically generate text captions. In this work, we analyze the new VizWiz dataset and compare it to the MSCOCO dataset, which is widely used for evaluating the performance of image captioning algorithms. We also implement and evaluate two state-of-the-art image caption models with accuracy, runtime, and resource analysis. Hopefully, our research will help the improvement of image captioning algorithms which focus on fulfilling the everyday needs of people with visual impairments
dc.description.departmentInformation
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/81211
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/8224
dc.language.isoen
dc.subjectImage captioning
dc.subjectComputer vision
dc.subjectDeep learning
dc.subjectPeople with visual impairments
dc.titleImage captioning algorithms for images taken by people with visual impairments
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentInformation
thesis.degree.disciplineInformation Studies
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Information Studies

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