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Computational Visual Media

, Volume 2, Issue 4, pp 367–377 | Cite as

Texture image classification with discriminative neural networks

  • Yang SongEmail author
  • Qing Li
  • Dagan Feng
  • Ju Jia Zou
  • Weidong Cai
Open Access
Research Article

Abstract

Texture provides an important cue for many computer vision applications, and texture image classification has been an active research area over the past years. Recently, deep learning techniques using convolutional neural networks (CNN) have emerged as the state-of-the-art: CNN-based features provide a significant performance improvement over previous handcrafted features. In this study, we demonstrate that we can further improve the discriminative power of CNN-based features and achieve more accurate classification of texture images. In particular, we have designed a discriminative neural network-based feature transformation (NFT) method, with which the CNN-based features are transformed to lower dimensionality descriptors based on an ensemble of neural networks optimized for the classification objective. For evaluation, we used three standard benchmark datasets (KTH-TIPS2, FMD, and DTD) for texture image classification. Our experimental results show enhanced classification performance over the state-of-the-art.

Keywords

texture classification neural networks feature learning feature transformation 

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Yang Song
    • 1
    Email author
  • Qing Li
    • 1
  • Dagan Feng
    • 1
  • Ju Jia Zou
    • 2
  • Weidong Cai
    • 1
  1. 1.School of Information Technologiesthe University of SydneySydneyAustralia
  2. 2.School of Computing, Engineering and MathematicsWestern Sydney UniversityPenrithAustralia

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