2026-09-19T15:59:43Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1006852026-06-06T00:54:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Antonio Torralba.
Raju, Akhil (Akhil G.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-01-04T20:53:21Z
2016-01-04T20:53:21Z
2015
2015
http://hdl.handle.net/1721.1/100685
933239294
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-59).
this thesis, I developed and carried out a procedure to measure the memorability of an image by running hundreds of human-trials and making use of a custom designed image dataset, the Mem60k dataset. The large store of ground-truth memorability data enabled a variety of insights and applications. The data revealed information about what qualities (emotional content, aesthetic appeal, etc.) in an image make it memorable. Convolutional neural networks (CNNs) trained on the data could predict an image's relative memorability with high accuracy. CNNs could also generate memorability heat maps which pinpoint which parts of an image are memorable. Finally, with additional usage of a massive image database, I designed a pipeline that could modify the intrinsic memorability of an image. The performance of each application was tested and measured by running further human trials.
by Akhil Raju.
M.Eng.
59 pages
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
Electrical Engineering and Computer Science.
Measuring and modifying the intrinsic memorability of images
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="62e0f810-a7e8-417f-86fd-94690a69603a">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Measuring and modifying the intrinsic memorability of images</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2015</PublicationDate>
<Authors>
<Author>
<DisplayName>Raju, Akhil (Akhil G.)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>this thesis, I developed and carried out a procedure to measure the memorability of an image by running hundreds of human-trials and making use of a custom designed image dataset, the Mem60k dataset. The large store of ground-truth memorability data enabled a variety of insights and applications. The data revealed information about what qualities (emotional content, aesthetic appeal, etc.) in an image make it memorable. Convolutional neural networks (CNNs) trained on the data could predict an image's relative memorability with high accuracy. CNNs could also generate memorability heat maps which pinpoint which parts of an image are memorable. Finally, with additional usage of a massive image database, I designed a pipeline that could modify the intrinsic memorability of an image. The performance of each application was tested and measured by running further human trials.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>