TEVC1.pdf (1.44 MB)

Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

Download (1.44 MB)
journal contribution
posted on 28.10.2020, 03:07 by Harith Al-Sahaf, A Al-Sahaf, Bing Xue, M Johnston, Mengjie Zhang
© 2016 IEEE. In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

History

Preferred citation

Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M. & Zhang, M. (2017). Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming. IEEE Transaction on Evolutionary Computation, 21(1), 83-101. https://doi.org/10.1109/TEVC.2016.2577548

Journal title

IEEE Transaction on Evolutionary Computation

Volume

21

Issue

1

Publication date

01/02/2017

Pagination

83-101

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

Online publication date

07/06/2016

ISSN

1089-778X

eISSN

1941-0026

Language

en

Exports