Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Prediction of photo shareability using supervisedlearning
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Many photos are taken every day. Some of them are shared to friends, acquaintances or family. Automatic prediction of what photos are shareable is investigated. The prediction techniques use supervised machine learning, trying to separate shareable images from non-shareable images. High performing prediction is shown to be hard to solve accurately using the chosen approach.

Abstract [sv]

Varje dag tas många fotografier. Några av dessa foton delas till vänner, bekanta eller familj. Automatisk förutsägelse av vilka fotografier som är delbara undersöks. Förutsägelseteknikerna försöker med hjälp av maskininlärning separera de delbara fotografierna från de som ej är delbara. Högpresterande förutsägning visas vara svårt att lösa med det valda tillvägagångssättet.

Place, publisher, year, edition, pages
2013.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-142431OAI: oai:DiVA.org:kth-142431DiVA: diva2:700508
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2014-03-11 Created: 2014-03-04 Last updated: 2014-03-11Bibliographically approved

Open Access in DiVA

fulltext(2628 kB)135 downloads
File information
File name FULLTEXT01.pdfFile size 2628 kBChecksum SHA-512
557af69db5312ed30628e268c52205b51f2b5ad36a57c94cee6fbec1f8d2ec2c0d218faa07b66c199b0acd2370baa04eef929885ad444029795ca37d9a2d158d
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 135 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 261 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf