SEAL2014.pdf (1.01 MB)

Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances

Download (1.01 MB)
conference contribution
posted on 27.10.2020, 21:48 by Harith Al-Sahaf, Mengjie Zhang, M Johnston
© Springer International Publishing Switzerland 2014. The task of image classification has been extensively studied due to its importance in a variety of domains such as computer vision and pattern recognition. Generally, the methods developed to perform this task require a large number of instances in order to build effective models. Moreover, the majority of those methods require human intervention to design and extract some good features. In this paper, we propose a Genetic Programming (GP) based method that evolves a program to perform the task of multiclass classification in texture images using only two instances of each class. The proposed method operates directly on raw pixel values, and does not require human intervention to perform feature extraction. The method is tested on two widely used texture data sets, and compared with two GP-based methods that also operate on raw pixel values, and six non-GP methods using three different types of domain-specific features. The results show that the proposed method significantly outperforms the other methods on both data sets.

History

Preferred citation

Al-Sahaf, H., Zhang, M. & Johnston, M. (2014, January). Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances. In Simulated Evolution and Learning 10th International Conference, SEAL 2014 Dunedin, New Zealand, December 15-18, 2014 Proceedings Simulated Evolution and Learning 10th International Conference, SEAL 2014, Dunedin, New Zealand (8886 pp. 335-346). Springer. https://doi.org/10.1007/978-3-319-13563-2_29

Conference name

Simulated Evolution and Learning 10th International Conference, SEAL 2014

Conference Place

Dunedin, New Zealand

Conference start date

15/12/2014

Conference finish date

18/12/2014

Title of proceedings

Simulated Evolution and Learning 10th International Conference, SEAL 2014 Dunedin, New Zealand, December 15-18, 2014 Proceedings

Volume

8886

Series

Lecture Notes in Computer Science

Contribution type

Published Paper

Publication or Presentation Year

01/01/2014

Pagination

335-346

Publisher

Springer

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349

Exports