ArticlePattern recognition2023
Invariance encoding in sliced-Wasserstein space for image classification with limited training data.
Article in Pattern recognition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Fast Rigid Alignment of Heterogeneous Images in Sliced Wasserstein Distance.SIAM journal on imaging sciences · 2026Article
- Linear Optimal Transport Subspaces for Point Set Classification.Journal of mathematical imaging and vision · 2025Article
- Local Sliced Wasserstein Feature Sets for Illumination Invariant Face Recognition.Pattern recognition · 2025Article
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- End-to-End Signal Classification in Signed Cumulative Distribution Transform Space.IEEE transactions on pattern analysis and machine intelligence · 2024Article
- Linear optimal transport subspaces for point set classification.Research square · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep convolutional neural networks (CNNs) are broadly considered to be state-of-the-art generic end-to-end image classification systems. However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective. Rather than using a data augmentation strategy to encode invariances as typically done in machine learning, here we propose to mathematically augment a nearest subspace classification model in sliced-Wasserstein space by exploiting certain mathematical properties of the Radon Cumulative Distribution Transform (R-CDT), a recently introduced image transform. We demonstrate that for a particular type of learning problem, our mathematical solution has advantages over data augmentation with deep CNNs in terms of classification accuracy and computational complexity, and is particularly effective under a limited training data setting. The method is simple, effective, computationally efficient, non-iterative, and requires no parameters to be tuned. Python code implementing our method is available at https://github.com/rohdelab/mathematical augmentation. Our method is integrated as a part of the software package PyTransKit, which is available at https://github.com/rohdelab/PyTransKit.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.