XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation

Nesteruk, Sergey; Illarionova, Svetlana; Akhtyamov, Timur; Shadrin, Dmitrii; Somov, Andrey; Pukalchik, Mariia; Oseledets, Ivan

doi:10.1109/access.2022.3154709

Cited by 16 publications

(10 citation statements)

References 58 publications

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“…The acquisition process of the water body distribution dataset in the Taitma Lake area is mainly divided into four parts: image preprocessing, sample generation, water body extraction, and accuracy evaluation (Figure 4). To reconcile computational demands with the capability of effectively capturing image features in deep semantic segmentation models such as U-Net, UPerNet, and DeepLabv3+, we segmented selected PlanetScope satellite imagery into 512 × 512 pixel tiles using a Python script [28,29]. This ensured that the models efficiently process high-resolution inputs while maintaining manageable computational loads, thereby optimizing performance and resource utilization.…”

Section: Water Extraction and Evaluation Methods (1) Image Data Acqui...mentioning

confidence: 99%

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Zi,

Wang,

et al. 2024

Remote Sensing

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Constructed from a dataset capturing the seasonal and annual water body distribution of the lower Qarqan River in the Taitma Lake area from 2017 to 2022, and combined with the meteorological and hydraulic engineering data, the spatial and temporal change patterns of the Taitma Lake watershed area were determined. Analyses were conducted using Planetscope (PS) satellite images and a deep learning model. The results revealed the following: ① Deep learning-based water body extraction provides significantly greater accuracy than the conventional water body index approach. With an impressive accuracy of up to 96.0%, UPerNet was found to provide the most effective extraction results among the three convolutional neural networks (U-Net, DeeplabV3+, and UPerNet) used for semantic segmentation; ② Between 2017 and 2022, Taitma Lake’s water area experienced a rapid decrease, with the distribution of water predominantly shifting towards the east–west direction more than the north–south. The shifts between 2017 and 2020 and between 2020 and 2022 were clearly discernible, with the latter stage (2020–2022) being more significant than the former (2017–2020); ③ According to observations, Taitma Lake’s changing water area has been primarily influenced by human activity over the last six years. Based on the research findings of this paper, it was observed that this study provides a valuable scientific basis for water resource allocation aiming to balance the development of water resources in the middle and upper reaches of the Tarim and Qarqan Rivers, as well as for the ecological protection of the downstream Taitma Lake.

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Section: Water Extraction and Evaluation Methods (1) Image Data Acqui...mentioning

confidence: 99%

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Zi,

Wang,

et al. 2024

Remote Sensing

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“…More recent advances in image augmentation include patch cutout, blurring, and image mixing where two training examples are blended using various methods [2,3]. The XtremeAugment method was also developed to generate new examples by adding one or more training objects with adjusted color or viewpoint, all on existing or new backgrounds [4].…”

Section: Related Workmentioning

confidence: 99%

Neural Network Architecture for Database Augmentation Using Shared Features

Sleeman¹,

Kapoor²,

Ghosh³

2023

Preprint

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The popularity of learning from data with machine learning and neural networks has lead to the creation of many new datasets for almost every problem domain. However, even within a single domain, these datasets are often collected with disparate features, sampled from different sub-populations, and recorded at different time points. Even with the plethora of individual datasets, large data science projects can be difficult as it is often not trivial to merge these smaller datasets. Inherent challenges in some domains such as medicine also makes it very difficult to create large single source datasets or multi-source datasets with identical features. Instead of trying to merge these non-matching datasets directly, we propose a neural network architecture that can provide data augmentation using features common between these datasets. Our results show that this style of data augmentation can work for both image and tabular data.

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“…In [23], a generic adversarial data augmentation model called AdvChain was proposed to enhance the diversity and efficiency of learning data for medical image segmentation. In [24], a new model called XtremeAugment was developed for labeling and augmenting images, using Hardware Dataset Augmentation (HAD) and Object-Based Augmentation (OBA). The HAD was used to enable users to acquire more data, whereas the OBA was used to increase the training data variability and maintain the distribution of the augmented images being similar to the actual data.…”

Section: Related Workmentioning

confidence: 99%

AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

Saraswathi

Pushpa

2023

Eng. Technol. Appl. Sci. Res.

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Artificial Intelligence (AI) is widely used in dermatology to analyze trichoscopy imaging and assess Alopecia Areata (AA) and scalp hair problems. From this viewpoint, the Attention-based Balanced Multi-Tasking Ensembling Deep (AB-MTEDeep) network was developed, which combined the Faster Residual Convolutional Neural Network (FRCNN) and Long Short-Term Memory (LSTM) network with cross residual learning to classify scalp images into different AA classes. This article presents a new data augmentation model called AA-Generative Adversarial Network (AA-GAN) to produce a huge number of images from a set of input images. The structure of AA-GAN and its loss functions are comparable to those of standard GAN, which encompasses a generator and a discriminator network. To generate high-quality AA structure-based images, the generator was trained to extract the 2D orientation and confidence maps along with the bust depth map from real hair and scalp images. The discriminator was also used to separate real from generated images, which were provided as feedback to the generator to create synthetic images that are extremely close to the real input images. The created images were used to train the AB-MTEDeep model for AA classification. Finally, the experimental results exhibited that the AA-GAN-AB-MTEDeep achieved 96.94% accuracy.

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XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation

Cited by 16 publications

References 58 publications

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Neural Network Architecture for Database Augmentation Using Shared Features

AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

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