Extended color local mapped pattern for color texture classification under varying illumination

Tamiris Trevisan Negri, Fang Zhou, Zoran Obradovic, Adilson Gonzaga

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper presents a color-texture descriptor based on the local mapped pattern approach for color-texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color-texture information from the images. These features are combined with texture information from the luminance image in a multiresolution fashion to get the ECLMP feature vector. The robustness of the proposed method is evaluated using the RawFooT, KTH-TIPS-2b, and USPtex databases. The experimental results show that the proposed descriptor is more robust to changes in the illumination condition than 22 alternative commonly used descriptors.

Original languageEnglish
Article number011008
JournalJournal of Electronic Imaging
Volume27
Issue number1
DOIs
StatePublished - 1 Jan 2018
Externally publishedYes

Keywords

  • accepted for publication Jan. 10, 2018
  • color texture
  • illumination
  • local descriptors
  • local mapped pattern
  • published online Feb. 5, 2018.
  • texture classification. Paper 170852SS received Sep. 29, 2017
  • texture description

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