Texture classification from single uncalibrated images: Random matrix theory approach

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Abstract

We studied the problem of classifying textured-materials from their single-imaged appearance, under general viewing and illumination conditions, using the theory of random matrices. To evaluate the performance of our algorithm, two distinct databases of images were used: The CUReT database and our database of colorectal polyp images collected from patients undergoing colon capsule endoscopy for early cancer detection. During the learning stage, our classifier algorithm established the universality laws for the empirical spectral density of the largest singular value and normalized largest singular value of the image intensity matrix adapted to the eigenvalues of the information-plus-noise model. We showed that these two densities converge to the generalized extreme value (GEV-Frechet) and Gaussian G 1 distribution with rate O(N1/2), respectively. To validate the algorithm, we introduced a set of unseen images to the algorithm. Misclassification rate of approximately 1%-6%, depending on the database, was obtained, which is superior to the reported values of 5%-45% in previous research studies.

Original languageEnglish
Title of host publication27th IEEE International Workshop on Machine Learning for Signal Processing
Number of pages6
PublisherIEEE Press
Publication date2017
ISBN (Print)978-1-5090-6342-0
ISBN (Electronic)978-1-5090-6341-3
DOIs
Publication statusPublished - 2017
Event27th International Workshop on Machine Learning for Signal Processing - Tokyo, Japan
Duration: 25. Sep 201728. Sep 2017
Conference number: 27

Workshop

Workshop27th International Workshop on Machine Learning for Signal Processing
Number27
Country/TerritoryJapan
CityTokyo
Period25/09/201728/09/2017
SeriesMachine Learning for Signal Processing
Volume2017
ISSN1551-2541

Keywords

  • Generalised extreme value (GEV)
  • Image processing
  • Random matrix theory
  • Texture classification
  • Tracy-Widom

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