University of Worcester Worcester Research and Publications

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

Al-Sahaf, H., Zhang, M., Al-Sahaf, A. and Johnston, Mark (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). pp. 825-844. ISSN Print: 1089-778X Online: 1941-0026

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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.

Item Type: Article
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Uncontrolled Discrete Keywords: classification, feature extraction, genetic programming, GP, image descriptor, keypoint detector, computation modelling, image edge detection, detectors
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: College of Health, Life and Environmental Sciences > School of Science and the Environment
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Depositing User: Mark Johnston
Date Deposited: 28 Apr 2019 13:46
Last Modified: 17 Jun 2020 17:28

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