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Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors

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posted on 28.10.2020, 04:42 by Harith Al-Sahaf, Mengjie Zhang, A Al-Sahaf, M Johnston
1089-778X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.

History

Preferred citation

Al-Sahaf, H., Zhang, M., Al-Sahaf, A. & Johnston, M. (2017). Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors. IEEE Transactions on Evolutionary Computation, 21(6), 825-844. https://doi.org/10.1109/TEVC.2017.2685639

Journal title

IEEE Transactions on Evolutionary Computation

Volume

21

Issue

6

Publication date

01/12/2017

Pagination

825-844

Publisher

IEEE

Publication status

Published

Contribution type

Article

Online publication date

22/03/2017

ISSN

1089-778X

eISSN

1941-0026

Language

en

Exports