Deep texture exemplar extraction based on trimmed T-CNN

Huisi Wu, Wei Yan, Ping Li, Zhenkun Wen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Texture exemplar has been widely used in synthesizing 3D movie scenes and appearances of virtual objects. Unfortunately, conventional texture synthesis methods usually only emphasized on generating optimal target textures with arbitrary sizes or diverse effects, and put little attention to automatic texture exemplar extraction. Obtaining texture exemplars is still a labor intensive task, which usually requires carefully cropping and post-processing. In this paper, we present an automatic texture exemplar extraction based on Trimmed Texture Convolutional Neural Network (Trimmed T-CNN). Specifically, our Trimmed T-CNN is filter banks for texture exemplar classification and recognition. Our Trimmed T-CNN is learned with a standard ideal exemplar dataset containing thousands of desired texture exemplars, which were collected and cropped by our invited artists. To efficiently identify the exemplar candidates from an input image, we employ a selective search algorithm to extract the potential texture exemplar patches. We then put all candidates into our Trimmed T-CNN for learning ideal texture exemplars based on our filter banks. Finally, optimal texture exemplars are identified with a scoring and ranking scheme. Our method is evaluated with various kinds of textures and user studies. Comparisons with different feature-based methods and different deep CNN architectures (AlexNet, VGG-M, Deep-TEN and FV-CNN) are also conducted to demonstrate its effectiveness.

Original languageEnglish
Pages (from-to)4502-4514
Number of pages13
JournalIEEE Transactions on Multimedia
Publication statusPublished - Dec 2021


  • deep learning
  • texture convolutional neural network
  • texture exemplar extraction
  • texture exemplar recognition
  • trimmed convolutional neural network

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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