liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Feature Extraction for Image Selection Using Machine Learning
Linköping University, Department of Electrical Engineering, Computer Vision.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Särdragsextrahering för bildurval vid användande av maskininlärning (Swedish)
Abstract [en]

During flights with manned or unmanned aircraft, continuous recording can result in avery high number of images to analyze and evaluate. To simplify image analysis and tominimize data link usage, appropriate images should be suggested for transfer and furtheranalysis. This thesis investigates features used for selection of images worthy of furtheranalysis using machine learning. The selection is done based on the criteria of havinggood quality, salient content and being unique compared to the other selected images.The investigation is approached by implementing two binary classifications, one regardingcontent and one regarding quality. The classifications are made using support vectormachines. For each of the classifications three feature extraction methods are performedand the results are compared against each other. The feature extraction methods used arehistograms of oriented gradients, features from the discrete cosine transform domain andfeatures extracted from a pre-trained convolutional neural network. The images classifiedas both good and salient are then clustered based on similarity measures retrieved usingcolor coherence vectors. One image from each cluster is retrieved and those are the resultingimages from the image selection. The performance of the selection is evaluated usingthe measures precision, recall and accuracy. The investigation showed that using featuresextracted from the discrete cosine transform provided the best results for the quality classification.For the content classification, features extracted from a convolutional neuralnetwork provided the best results. The similarity retrieval showed to be the weakest partand the entire system together provides an average accuracy of 83.99%.

Place, publisher, year, edition, pages
2017. , p. 45
Keywords [en]
Machine learning, Feature extraction, Automatic selection
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-142095ISRN: LiTH-ISY-EX--17/5097--SEOAI: oai:DiVA.org:liu-142095DiVA, id: diva2:1151145
External cooperation
Saab AB
Subject / course
Computer Vision Laboratory
Supervisors
Examiners
Available from: 2017-10-23 Created: 2017-10-22 Last updated: 2017-10-23Bibliographically approved

Open Access in DiVA

fulltext(2183 kB)107 downloads
File information
File name FULLTEXT01.pdfFile size 2183 kBChecksum SHA-512
8f1958a6aa6ab8b2229e20a96875f834b0cc59e28d370bda4b55f407c0f5b64ef783dd576b61f1c0233a4a03373cef10ec47eed293c2d394aba4941d108544cd
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Lorentzon, Matilda
By organisation
Computer Vision
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 107 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: 151 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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