Learning Robust Feature Descriptor for Image Registration With Genetic Programming

The robustness and accuracy of feature descriptor are two essential factors in the process of image registration. Existing feature descriptors can extract important image features, but it may be difficult to find enough correct correspondences for sophisticated images. And these feature descriptors...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.39389-39402
Main Authors: Wu, Yue, Su, Qingxiu, Ma, Wenping, Liu, Shaodi, Miao, Qiguang
Format: Article
Language:English
Subjects:
feature descriptor
Feature extraction
Genetic algorithms
Genetic programming
Geometric transformation
Image registration
Lighting
Noise reduction
Registration
Remote sensing
Robustness
scale-invariant feature transform (SIFT)
Sociology
Statistics
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Summary:The robustness and accuracy of feature descriptor are two essential factors in the process of image registration. Existing feature descriptors can extract important image features, but it may be difficult to find enough correct correspondences for sophisticated images. And these feature descriptors often require domain expertise and human intervention. The aim of this paper is to utilise Genetic Programming (GP) to automatically evolve feature descriptors which are adaptive to various images including remote sensing images and optical images. In this paper, a novel GP-based method (GPFD) is proposed to extract feature vectors and evolve image descriptors for image registration without supervision. The proposed method designs a set of simple arithmetic operators and first-order statistics to construct feature descriptors in order to reduce noise interference. The performance of the proposed method is evaluated and compared against five methods including SIFT, SURF, RIFT, GLPM and GP. These results demonstrate that the feature descriptors evolved by GPFD are robust to complex geometric transformation, the illumination difference and noise.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2968339