Enhancing Image Classification with Augmentation: Data Augmentation Techniques for Improved Image Classification

• URL: http://arxiv.org/abs/2502.18691v1
• Date: Tue, 25 Feb 2025 23:03:30 GMT
• Title: Enhancing Image Classification with Augmentation: Data Augmentation Techniques for Improved Image Classification
• Authors: Saorj Kumar, Prince Asiamah, Oluwatoyin Jolaoso, Ugochukwu Esiowu,
• Abstract summary: Convolutional Neural Networks (CNNs) serve as the workhorse of deep learning, finding applications in various fields that rely on images.<n>In this study, we explore the effectiveness of 11 different sets of data augmentation techniques, which include three novel sets proposed in this work.<n>The ensemble of image augmentation techniques proposed emerges as the most effective on the Caltech-101 dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNNs) serve as the workhorse of deep learning, finding applications in various fields that rely on images. Given sufficient data, they exhibit the capacity to learn a wide range of concepts across diverse settings. However, a notable limitation of CNNs is their susceptibility to overfitting when trained on small datasets. The augmentation of such datasets can significantly enhance CNN performance by introducing additional data points for learning. In this study, we explore the effectiveness of 11 different sets of data augmentation techniques, which include three novel sets proposed in this work. The first set of data augmentation employs pairwise channel transfer, transferring Red, Green, Blue, Hue, and Saturation values from randomly selected images in the database to all images in the dataset. The second set introduces a novel occlusion approach, where objects in the images are occluded by randomly selected objects from the dataset. The third set involves a novel masking approach, using vertical, horizontal, circular, and checkered masks to occlude portions of the images. In addition to these novel techniques, we investigate other existing augmentation methods, including rotation, horizontal and vertical flips, resizing, translation, blur, color jitter, and random erasing, and their effects on accuracy and overfitting. We fine-tune a base EfficientNet-B0 model for each augmentation method and conduct a comparative analysis to showcase their efficacy. For the evaluation and comparison of these augmentation techniques, we utilize the Caltech-101 dataset. The ensemble of image augmentation techniques proposed emerges as the most effective on the Caltech-101 dataset. The results demonstrate that diverse data augmentation techniques present a viable means of enhancing datasets for improved image classification.

Related papers

• Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model [5.57325257338134]
  Traditional data augmentation methods cannot alter high-level semantic attributes. We propose a text-to-image diffusion model to parameterize image-to-image transformations. We achieve this goal by erasing instances of real objects from the original dataset and generating new instances with similar semantics in the erased regions.
  arXiv  Detail & Related papers  (2024-09-30T10:21:54Z)
• Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior [63.64088590653005]
  We propose Diff-Mosaic, a data augmentation method based on the diffusion model. We introduce an enhancement network called Pixel-Prior, which generates highly coordinated and realistic Mosaic images. In the second stage, we propose an image enhancement strategy named Diff-Prior. This strategy utilizes diffusion priors to model images in the real-world scene.
  arXiv  Detail & Related papers  (2024-06-02T06:23:05Z)
• Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model [80.61157097223058]
  A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. We introduce an innovative inter-class data augmentation method known as Diff-Mix, which enriches the dataset by performing image translations between classes.
  arXiv  Detail & Related papers  (2024-03-28T17:23:45Z)
• Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification [57.1795052451257]
  We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples. We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems. The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
  arXiv  Detail & Related papers  (2024-01-26T08:28:13Z)
• Image edge enhancement for effective image classification [7.470763273994321]
  We propose an edge enhancement-based method to enhance both accuracy and training speed of neural networks. Our approach involves extracting high frequency features, such as edges, from images within the available dataset and fusing them with the original images.
  arXiv  Detail & Related papers  (2024-01-13T10:01:34Z)
• T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator [0.0]
  This paper proposes an Adaptive Data Augmentation Framework based on the tensor T-product Operator. It triples one image data to be trained and gain the result from all these three images together with only less than 0.1% increase in the number of parameters. Numerical experiments show that our data augmentation framework can improve the performance of original neural network model by 2%.
  arXiv  Detail & Related papers  (2023-06-07T08:30:44Z)
• Effective Data Augmentation With Diffusion Models [65.09758931804478]
  We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.
  arXiv  Detail & Related papers  (2023-02-07T20:42:28Z)
• Augmentation Learning for Semi-Supervised Classification [13.519613713213277]
  We propose a Semi-Supervised Learning method that automatically selects the most effective data augmentation policy for a particular dataset. We show how policy learning can be used to adapt augmentations to datasets beyond ImageNet.
  arXiv  Detail & Related papers  (2022-08-03T10:06:51Z)
• Feature transforms for image data augmentation [74.12025519234153]
  In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method.
  arXiv  Detail & Related papers  (2022-01-24T14:12:29Z)
• Fusion of CNNs and statistical indicators to improve image classification [65.51757376525798]
  Convolutional Networks have dominated the field of computer vision for the last ten years. Main strategy to prolong this trend relies on further upscaling networks in size. We hypothesise that adding heterogeneous sources of information may be more cost-effective to a CNN than building a bigger network.
  arXiv  Detail & Related papers  (2020-12-20T23:24:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.