Automatic texture exemplar extraction based on a novel textureness metric

Huisi Wu, Junrong Jiang, Ping Li, Zhenkun Wen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Traditional texture synthesis methods usually emphasized the final effect of the target textures. However, none of them focus on auto-extraction of the source texture exemplar. In this paper, we present a novel textureness metric based on Gist descriptor to accurately extract texture exemplar from an arbitrary image including texture regions. Our method emphasizes the importance of the exemplar for the example-based texture synthesis and focus on ideal texture exemplar auto-extraction. To improve the efficiency of the texture patch searching, we perform a Poisson disk sampling to crop exemplar randomly and uniformly from images. To improve the accuracy of texture recognition, we also use a SVM for the UIUC database to distinguish the texture regions and non-texture regions. The proposed method is evaluated on a variety of images with different kinds of textures. Convincing visual and statistics results demonstrated its effectiveness.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Qingming Huang, Abdulmotaleb El Saddik, Shuqiang Jiang, Xiaopeng Fan
Number of pages9
ISBN (Print)9783319773827
Publication statusPublished - Sept 2017
Externally publishedYes
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sept 201729 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10736 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th Pacific-Rim Conference on Multimedia, PCM 2017


  • Synthesizability
  • Texture exemplar
  • Texture feature
  • Textureness

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Automatic texture exemplar extraction based on a novel textureness metric'. Together they form a unique fingerprint.

Cite this