Image Registration of Very Large Images via Genetic Programming

Chicotay, Sarit; David, Omid E.; Netanyahu, Nathan S.

doi:10.1109/cvprw.2014.56

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Only few researchers have employed GP for image registration. Chicotay et al [29] presented GP based approach for large size image registration, in which transformation T on an image mapped every pixel ( , ) p x y  of the input image to a different pixel ( ', ') p x y in the coordinate system of the referenced image. Mutual Information (MI), was used as a measure to search for a function that generated highest value, when there existed maximum overlap between the referenced and the transformed image.…”

Section: Gp In Image Registrationmentioning

confidence: 99%

A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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Genetic programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree‐type structure. Similarly, research is also gaining momentum in the field of image processing, because of its promising results over vast areas of applications ranging from medical image processing to multispectral imaging. Image processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, that is, complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for image processing since its inception. Many interesting GP techniques have been developed and employed in the field of image processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in image processing and provides useful resources for further research. In addition, the comparison of different parameters used in different applications of image processing is summarized in tabular form. Moreover, analysis of the different parameters used in image processing related tasks is carried‐out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in image processing but also in other fields, may increase. In addition, guidelines are provided for applying GP in image processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.

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Section: Gp In Image Registrationmentioning

confidence: 99%

A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Since the late 1990s, Genetic Programming (GP) [26] has been used to automatically evolve/extract/construct image keypoints and features [5], [27]- [35], showing good potential in this direction. The method proposed by Ebner and Zell [36] is one of the earliest works employing GP to automatically evolve an interest point detector.…”

Section: Introductionmentioning

confidence: 99%

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

Al-Sahaf

Xue

et al. 2016

IEEE Trans. Evol. Computat.

113

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In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domainexpert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

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“…image regeneration [52,161], and image processing [118]. The GP methods proposed in the field of computer vision can be categorised differently based on different criteria, e.g., in terms of the task, the domain, and the program structure.…”

Section: Genetic Programming For Image Classificationmentioning

confidence: 99%

“…Since the late 1990s, GP has been used to automatically evolve/construct image keypoints and features [241,74,1,6,10,337,282,169,170,52], showing good potential in this direction. The method proposed by Ebner and Zell [80] is one of the earliest works employing GP to automatically evolve an interest point detector.…”

Section: Evolutionary-based Image Descriptorsmentioning

confidence: 99%

Genetic Programming for Automatically Synthesising Robust Image Descriptors with A Small Number of Instances

Al-Sahaf¹

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<p>Image classification is a core task in many applications of computer vision, including object detection and recognition. It aims at analysing the visual content and automatically categorising a set of images into different groups. Performing image classification can largely be affected by the features used to perform this task. Extracting features from images is a challenging task due to the large search space size and practical requirements such as domain knowledge and human intervention. Human intervention is usually needed to identify a good set of keypoints (regions of interest), design a set of features to be extracted from those keypoints such as lines and corners, and develop a way to extract those features. Automating these tasks has great potential to dramatically decrease the time and cost, and may potentially improve the performance of the classification task. There are two well-recognised approaches in the literature to automate the processes of identifying keypoints and extracting image features. Designing a set of domain-independent features is the first approach, where the focus is on dividing the image into a number of predefined regions and extracting features from those regions. The second approach is synthesising a function or a set of functions to form an image descriptor that aims at automatically detecting a set of keypoints such as lines and corners, and performing feature extraction. Although employing image descriptors is more effective and very popular in the literature, designing those descriptors is a difficult task that in most cases requires domain-expert intervention. The overall goal of this thesis is to develop a new domain independent Genetic Programming (GP) approach to image classification by utilising GP to evolve programs that are capable of automatically detecting diverse and informative keypoints, designing a set of features, and performing feature extraction using only a small number of training instances to facilitate image classification, and are robust to different image changes such as illumination and rotation. This thesis focuses on incorporating a variety of simple arithmetic operators and first-order statistics (mid-level features) into the evolutionary process and on representation of GP to evolve programs that are robust to image changes for image classification. This thesis proposes methods for domain-independent binary classification in images using GP to automatically identify regions within an image that have the potential to improve classification while considering the limitation of having a small training set. Experimental results show that in over 67% of cases the new methods significantly outperform the use of existing hand-crafted features and features automatically detected by other methods. This thesis proposes the first GP approach for automatically evolving an illumination-invariant dense image descriptor that detects automatically designed keypoints, and performs feature extraction using only a few instances of each class. The experimental results show improvement of 86% on average compared to two GP-based methods, and can significantly outperform domain-expert hand-crafted descriptors in more than 89% of the cases. This thesis also considers rotation variation of images and proposes a method for automatically evolving rotation-invariant image descriptors through integrating a set of first-order statistics as terminals. Compared to hand-crafted descriptors, the experimental results reveal that the proposed method has significantly better performance in more than 83% of the cases. This thesis proposes a new GP representation that allows the system to automatically choose the length of the feature vector side-by-side with evolving an image descriptor. Automatically determining the length of the feature vector helps to reduce the number of the parameters to be set. The results show that this method has evolved descriptors with a very small feature vector which yet still significantly outperform the competitive methods in more than 91% of the cases. This thesis proposes a method for transfer learning by model in GP, where an image descriptor evolved on instances of a related problem (source domain) is applied directly to solve a problem being tackled (target domain). The results show that the new method evolves image descriptors that have better generalisability compared to hand-crafted image descriptors. Those automatically evolved descriptors show positive influence on classifying the target domain datasets in more than 56% of the cases.</p>

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Image Registration of Very Large Images via Genetic Programming

Abstract: Image registration (IR) is a fundamental task in image

Cited by 4 publications

References 19 publications

A recent survey on the applications of genetic programming in image processing

A recent survey on the applications of genetic programming in image processing

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

Genetic Programming for Automatically Synthesising Robust Image Descriptors with A Small Number of Instances

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