Classification of images using semi-supervised learning and structural similarity measure

Hubert Cecotti, Bryan Gardiner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we evaluate the performance of graph-based semi supervised learning (SSL) for the clas- sification of images, by using the structural similarity index measure (SSIM) to build the adjacency matrix of the graph. Performance evaluation was carried out with the TID2013 database. The results support the conclusion that SSIM can be efficiently used with graph-based SSL to retrieve images that are similar.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages67-68
Number of pages2
Publication statusPublished - 26 Aug 2016
EventIrish Machine Vision and Image Processing conference - NUI Galway, Ireland
Duration: 26 Aug 2016 → …

Conference

ConferenceIrish Machine Vision and Image Processing conference
Period26/08/16 → …

Fingerprint

Supervised learning

Keywords

  • Image processing
  • Semi-supervised learning
  • Structural Similarity Measure.

Cite this

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title = "Classification of images using semi-supervised learning and structural similarity measure",
abstract = "In this paper, we evaluate the performance of graph-based semi supervised learning (SSL) for the clas- sification of images, by using the structural similarity index measure (SSIM) to build the adjacency matrix of the graph. Performance evaluation was carried out with the TID2013 database. The results support the conclusion that SSIM can be efficiently used with graph-based SSL to retrieve images that are similar.",
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Cecotti, H & Gardiner, B 2016, Classification of images using semi-supervised learning and structural similarity measure. in Unknown Host Publication. pp. 67-68, Irish Machine Vision and Image Processing conference, 26/08/16.

Classification of images using semi-supervised learning and structural similarity measure. / Cecotti, Hubert; Gardiner, Bryan.

Unknown Host Publication. 2016. p. 67-68.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper, we evaluate the performance of graph-based semi supervised learning (SSL) for the clas- sification of images, by using the structural similarity index measure (SSIM) to build the adjacency matrix of the graph. Performance evaluation was carried out with the TID2013 database. The results support the conclusion that SSIM can be efficiently used with graph-based SSL to retrieve images that are similar.

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