Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images

• URL: http://arxiv.org/abs/2311.11029v1
• Date: Sat, 18 Nov 2023 10:35:18 GMT
• Title: Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
• Authors: Nam Cao, Olga Saukh
• Abstract summary: We leverage the domain knowledge that geometric features are highly important for accurate pollen identification. We introduce two novel geometric image augmentation techniques to significantly narrow the accuracy gap between the model performance on the train and test datasets.
• Score: 4.545340728210854
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Distribution shifts are characterized by differences between the training and test data distributions. They can significantly reduce the accuracy of machine learning models deployed in real-world scenarios. This paper explores the distribution shift problem when classifying pollen grains from microscopic images collected in the wild with a low-cost camera sensor. We leverage the domain knowledge that geometric features are highly important for accurate pollen identification and introduce two novel geometric image augmentation techniques to significantly narrow the accuracy gap between the model performance on the train and test datasets. In particular, we show that Tenengrad and ImageToSketch filters are highly effective to balance the shape and texture information while leaving out unimportant details that may confuse the model. Extensive evaluations on various model architectures demonstrate a consistent improvement of the model generalization to field data of up to 14% achieved by the geometric augmentation techniques when compared to a wide range of standard image augmentations. The approach is validated through an ablation study using pollen hydration tests to recover the shape of dry pollen grains. The proposed geometric augmentations also receive the highest scores according to the affinity and diversity measures from the literature.

Related papers

• Data Augmentation via Latent Diffusion for Saliency Prediction [67.88936624546076]
  Saliency prediction models are constrained by the limited diversity and quantity of labeled data. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
  arXiv  Detail & Related papers  (2024-09-11T14:36:24Z)
• Select-Mosaic: Data Augmentation Method for Dense Small Object Scenes [4.418515380386838]
  Mosaic data augmentation technique stitches multiple images together to increase the diversity and complexity of training data. This paper proposes the Select-Mosaic data augmentation method, which is improved with a fine-grained region selection strategy. The improved Select-Mosaic method demonstrates superior performance in handling dense small object detection tasks.
  arXiv  Detail & Related papers  (2024-06-08T09:22:08Z)
• Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency [25.632973225129728]
  We study the geometric properties of the diffusion model, whose forward diffusion process and reverse generation process construct a series of distributions on a manifold. We show that the generation rate is highly correlated with intuitive visual properties, such as visual saliency, of the image component. We propose an efficient and differentiable scheme to estimate the generation rate for a given image component over time, giving rise to a generation curve.
  arXiv  Detail & Related papers  (2024-06-07T07:32:41Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation [2.8747398859585376]
  We trialled different methods of data preparation for CNN training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted through trialling and ranking various training patch selection sampling strategies, patch and batch sizes and data augmentations and scaling. We fully trained five models on the 2018 training image and applied the model to the 2015 test image with the output LULC classifications achieving an average of 0.84 user accuracy of 0.81 and producer accuracy of 0.87.
  arXiv  Detail & Related papers  (2023-04-28T04:38:45Z)
• 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)
• Robustifying Deep Vision Models Through Shape Sensitization [19.118696557797957]
  We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures.
  arXiv  Detail & Related papers  (2022-11-14T11:17:46Z)
• Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images [3.6868861317674524]
  Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Traditional methods are based on limited sampling in the field and analysis in laboratory. We propose a method for estimating parsley seed maturity using multispectral UAV imagery, with a new approach for automatic data labeling.
  arXiv  Detail & Related papers  (2022-08-09T09:06:51Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.