Skip to main content
DORAS
DCU Online Research Access Service
Login (DCU Staff Only)
Using filter banks in Convolutional Neural Networks for texture classification

Andrearczyk, Vincent and Whelan, Paul F. ORCID: 0000-0001-9230-7656 (2016) Using filter banks in Convolutional Neural Networks for texture classification. Pattern Recognition Letters, 84 . pp. 63-69. ISSN 0167-8655

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
239kB

Abstract

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:computer vision; image analysis; Texture classification; Convolutional Neural Networks; dense orderless pooling; filter banks
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Publisher:Elsevier
Official URL:https://doi.org/10.1016/j.patrec.2016.08.016
Copyright Information:© 2016 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:22094
Deposited On:27 Oct 2017 13:32 by Paul Whelan . Last Modified 11 Jan 2019 10:31

Downloads

Downloads per month over past year

Archive Staff Only: edit this record

Altmetric
- Altmetric
+ Altmetric
  • Student Email
  • Staff Email
  • Student Apps
  • Staff Apps
  • Loop
  • Disclaimer
  • Privacy
  • Contact Us