Learning Robust Feature Descriptor for Image Registration with Genetic Programming

Yue Wu, Qingxiu Su, Wenping Ma, Shaodi Liu, Qiguang Miao
2020 IEEE Access  
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
more » ... luding 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. INDEX TERMS Image registration, genetic programming, feature descriptor, scale-invariant feature transform (SIFT).
doi:10.1109/access.2020.2968339 fatcat:lzm76l4bvrez7gaubiqn53encu