A closer look at texture metrics for visualization

Haleh Hagh-Shenas, Victoria Interrante, Cheong Hee Park

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

2 Scopus citations


This paper presents some insights into perceptual metrics for texture pattern categorization. An increasing number of researchers in the field of visualization are trying to exploit texture patterns to overcome the innate limitations of three dimensional color spaces. However, a comprehensive understanding of the most important features by which people group textures is essential for effective texture utilization in visualization. There have been a number of studies aiming at finding the perceptual dimensions of the texture. However, in order to use texture for multivariate visualization we need to first realize the circumstances under which each of these classification holds. In this paper we discuss the results of our three recent studies intended to gain greater insight into perceptual texture metrics. The first and second experiments investigate the role that orientation, scale and contrast play in characterizing a texture pattern. The third experiment is designed to understand the perceptual rules people utilize in arranging texture patterns based on the perceived directionality. Finally, in our last section we present our current effort in designing a computational method which orders the input textures based on directionality and explain its correlation with the human study.

Original languageEnglish (US)
Title of host publicationHuman Vision and Electronic Imaging XI - Proceedings of SPIE-IS and T Electronic Imaging
StatePublished - 2006
EventHuman Vision and Electronic Imaging XI - San Jose, CA, United States
Duration: Jan 16 2006Jan 18 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceHuman Vision and Electronic Imaging XI
Country/TerritoryUnited States
CitySan Jose, CA


  • Filtering
  • Texture classification
  • Texture metrics
  • Visualization


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