Learning Robust Feature Descriptor for Image Registration With Genetic Programming

Wu, Yue; Su, Qingxiu; Ma, Wenping; Liu, Shaodi; Miao, Qiguang

doi:10.1109/access.2020.2968339

Cited by 10 publications

(3 citation statements)

References 41 publications

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“…Other interesting approaches that are related to our research are [51], [52]. In particular, [51] proposes a novel GPbased method (GPFD) to extract feature vectors and evolve image descriptors for image registration without supervision. The method designs a set of simple arithmetic operators and first-order statistics to construct feature descriptors in order to reduce noise interference.…”

Section: Algorithm 4 Binary Lookup Fitness Computationmentioning

confidence: 99%

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

2021

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In this paper we describe a family of scan-matching based registration algorithms for tracking moving objects which fall in the emerging area that predicates the integration between robotics and big data applications. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. Algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed family of algorithms is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, algorithms are also very fast thanks to the genetic pre-alignment task and the novel metrics we propose.

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Section: Algorithm 4 Binary Lookup Fitness Computationmentioning

confidence: 99%

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

2021

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“…The fundamental concepts of SURF come from SIFT to improve computational efficiency [18]. In this algorithm, the image needs to be converted first to grayscale.…”

Section: Surfmentioning

confidence: 99%

A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Kok

Rajendran

2021

Symmetry

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Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

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“…However, this method could learn a small number of features from the data. Wu et al [228] applied GP to automatically evolve image descriptors that can extract features from various images for image registration. This GP method employed a set of simple arithmetic operators as functions and the statistics of the region as terminals to evolve descriptors, which can reduce noise interference of images.…”

Section: Gp For Feature Learning In Other Tasksmentioning

confidence: 99%

Genetic Programming for Feature Learning in Image Classification

Depositor¹

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Image classification is an important and fundamental task in computer vision and machine learning. The task is to classify images into one of some pre-defined groups based on the content in the images. However, image classification is a challenging task due to high variations across images, such as illumination, viewpoint, scale variations, deformation, and occlusion. To effectively solve image classification, it is necessary to extract or learn a set of meaningful features from raw pixels or images. The effectiveness of these features significantly affects classification performance. Feature learning aims to automatically learn effective features from images for classification. However, feature learning is difficult due to the high variations of images and the large search space. Genetic Programming (GP) as an Evolutionary Computation (EC) technique is known for its powerful global search ability and high interpretability of the evolved solutions. Compared with other EC methods, GP has a flexible representation of variable length and can search the solution space without any assumptions on the solution structure. The potential of GP in feature learning for image classification has not been comprehensively investigated due to the use of simple representations, e.g., functions and program structures. The overall goal of this thesis is to further investigate and explore the potential of GP for image classification by developing a new GP-based approach with a new representation to automatically learning effective features for different types of image classification tasks. Firstly, this thesis proposes a new GP-based approach with image descriptors to learning global and/or local features for image classification by developing a new program structure, a new function set, a new terminal set, and a new fitness function. These new designs allow GP to detect small regions from the relatively large input image, extract features using image descriptors from the detected regions or the input image, and combine the extracted features for classification. The results show that the new approach significantly outperforms five GP-based methods, eight traditional methods, and three convolutional neural network methods in almost all the comparisons on eight different datasets. Secondly, this thesis proposes a new GP-based approach with a flexible program structure and image-related operators for feature learning in image classification. The new approach learns effective features transformed by multiple layers, i.e., a filtering layer, a pooling layer, a feature extraction layer, and a feature concatenation layer, in a flexible way. The results show that the new approach achieves better performance than a large number of effective methods on 12 benchmark datasets. The solutions and features learned by the new approach provide high interpretability. Thirdly, this thesis proposes the first GP-based approach to automatically and simultaneously learning features and evolving ensembles for image classification. The new approach can learn high-level features through multiple transformations, select effective classification algorithms and optimise the parameters for these classification algorithms to build effective ensembles. The new approach outperforms a large number of benchmark methods on 12 different image classification datasets. Finally, this thesis proposes a multi-population GP-based approach with knowledge transfer and ensembles to improving both the generalisation performance and computational efficiency of GP-based feature learning algorithms for image classification. The new approach can achieve better generalisation performance and computational efficiency than baseline GP-based feature learning method. The new approach can achieve better performance on 11 datasets than a large number of benchmark methods, including many neural network-based methods.

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Learning Robust Feature Descriptor for Image Registration With Genetic Programming

Cited by 10 publications

References 41 publications

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Genetic Programming for Feature Learning in Image Classification

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