The Importance of Phase to Texture Discrimination and Similarity

Xinghui Dong, Ying Gao, Junyu Dong, Mike Chantler

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Abstract

In this article, we investigate the importance of phase for texture discrimination and similarity estimation tasks. We first use two psychophysical experiments to investigate the relative importance of phase and magnitude spectra for human texture discrimination and similarity estimation. The results show that phase is more important to humans for both tasks. We further examine the ability of 51 computational feature sets to perform these two tasks. In contrast with the psychophysical experiments, it is observed that the magnitude data is more important to these computational feature sets than the phase data. We hypothesise that this inconsistency is due to the difference between the abilities of humans and the computational feature sets to utilise phase data. This motivates us to investigate the application of the 51 feature sets to phase-only images in addition to their use on the original data set. This investigation is extended to exploit Convolutional Neural Network (CNN) features. The results show that our feature fusion scheme improves the average performance of those feature sets for estimating humans' perceptual texture similarity. The superior performance should be attributed to the importance of phase to texture similarity.

Original languageEnglish
Pages (from-to)3755-3768
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number9
Early online date18 Mar 2020
DOIs
Publication statusPublished - 1 Sep 2021

Keywords

  • Fourier magnitude
  • Fourier phase
  • texture discrimination
  • texture features
  • texture similarity

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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