Comparative Review of Machine Learning and Deep Learning Techniques for Texture Classification
Authors
Shantanu Kumar1, *, Amey Gupta2
1Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad, India
2Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Pilani, India
*Corresponding author.
Email: f20190375@hyderabad.bits-pilani.ac.in
Corresponding Author
Shantanu Kumar
Available Online 5 December 2022.
- DOI
- 10.2991/978-94-6463-074-9_10How to use a DOI?
- Keywords
- Texture Classification; Gabor Filter; Convolutional Neural Networks; Feature Extraction; Image Processing
- Abstract
In this paper, we present a review and analysis of different methods and resources for texture classification, presenting the most popular techniques that have been used in the past decade. The paper covers some of the most traditional approaches involving texture descriptors like Gray Level Co-occurrence Matrix and Gabor Filter Banks, to some of more recent approaches such as Convolutional Neural Network (CNN) and ScatNets. These methods are tested and their performance is evaluated on six standard datasets.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shantanu Kumar AU - Amey Gupta PY - 2022 DA - 2022/12/05 TI - Comparative Review of Machine Learning and Deep Learning Techniques for Texture Classification BT - Proceedings of the International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES 2022) PB - Atlantis Press SP - 95 EP - 112 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-074-9_10 DO - 10.2991/978-94-6463-074-9_10 ID - Kumar2022 ER -