126 research outputs found
Data Augmentation using Random Image Cropping and Patching for Deep CNNs
Deep convolutional neural networks (CNNs) have achieved remarkable results in
image processing tasks. However, their high expression ability risks
overfitting. Consequently, data augmentation techniques have been proposed to
prevent overfitting while enriching datasets. Recent CNN architectures with
more parameters are rendering traditional data augmentation techniques
insufficient. In this study, we propose a new data augmentation technique
called random image cropping and patching (RICAP) which randomly crops four
images and patches them to create a new training image. Moreover, RICAP mixes
the class labels of the four images, resulting in an advantage similar to label
smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., the
shake-shake regularization model) by comparison with competitive data
augmentation techniques such as cutout and mixup. RICAP achieves a new
state-of-the-art test error of on CIFAR-10. We also confirmed that
deep CNNs with RICAP achieve better results on classification tasks using
CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft
COCO.Comment: accepted version, 16 page
HFRAS : design of a high-density feature representation model for effective augmentation of satellite images
Efficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy & precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM & GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed
Dataset Pre-Processing and Artificial Augmentation, Network Architecture and Training Parameters used in Appropriate Training of Convolutional Neural Networks for Classification Based Computer Vision Applications: A Survey
Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index
Urban rivers provide a water environment that influences residential living.
River surface monitoring has become crucial for making decisions about where to
prioritize cleaning and when to automatically start the cleaning treatment. We
focus on the organic mud, or "scum", that accumulates on the river's surface
and contributes to the river's odor and has external economic effects on the
landscape. Because of its feature of a sparsely distributed and unstable
pattern of organic shape, automating the monitoring process has proved
difficult. We propose a patch-wise classification pipeline to detect scum
features on the river surface using mixture image augmentation to increase the
diversity between the scum floating on the river and the entangled background
on the river surface reflected by nearby structures like buildings, bridges,
poles, and barriers. Furthermore, we propose a scum-index cover on rivers to
help monitor worse grade online, collect floating scum, and decide on chemical
treatment policies. Finally, we demonstrate the application of our method on a
time series dataset with frames every ten minutes recording river scum events
over several days. We discuss the significance of our pipeline and its
experimental findings.Comment: 15 figures, 3 tabl
Image Data Augmentation Approaches: A Comprehensive Survey and Future directions
Deep learning (DL) algorithms have shown significant performance in various
computer vision tasks. However, having limited labelled data lead to a network
overfitting problem, where network performance is bad on unseen data as
compared to training data. Consequently, it limits performance improvement. To
cope with this problem, various techniques have been proposed such as dropout,
normalization and advanced data augmentation. Among these, data augmentation,
which aims to enlarge the dataset size by including sample diversity, has been
a hot topic in recent times. In this article, we focus on advanced data
augmentation techniques. we provide a background of data augmentation, a novel
and comprehensive taxonomy of reviewed data augmentation techniques, and the
strengths and weaknesses (wherever possible) of each technique. We also provide
comprehensive results of the data augmentation effect on three popular computer
vision tasks, such as image classification, object detection and semantic
segmentation. For results reproducibility, we compiled available codes of all
data augmentation techniques. Finally, we discuss the challenges and
difficulties, and possible future direction for the research community. We
believe, this survey provides several benefits i) readers will understand the
data augmentation working mechanism to fix overfitting problems ii) results
will save the searching time of the researcher for comparison purposes. iii)
Codes of the mentioned data augmentation techniques are available at
https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work
will spark interest in research community.Comment: We need to make a lot changes to make its quality bette
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