Structural Texture Similarity for Material Recognition

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

1 Scopus citations

Abstract

We propose a new direct approach for material recognition under diverse illumination and viewing conditions based on visual texture. We apply K-means clustering to feature vectors that consist of steerable filter subband statistics and dominant colors of each texture image in order to obtain a small number of exemplars characterizing each material. We then use structural texture similarity metrics and color composition metrics to compare a query texture to the exemplars for material classification. Experimental results using the CUReT database establish the importance of color and demonstrate that five exemplars per texture provide performance comparable to the state of the art.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages4424-4428
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period9/22/199/25/19

Keywords

  • dominant colors
  • material identification
  • subband statistics
  • Texture analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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