ArticleJournal of mathematical imaging and vision2021
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.
Article in Journal of mathematical imaging and vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- 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
- Anatomy-Specific Classification Model Using Label-Free FLIm to Aid Intraoperative Surgical Guidance of Head and Neck Cancer.IEEE transactions on bio-medical engineering · 2023Article
- Invariance encoding in sliced-Wasserstein space for image classification with limited training data.Pattern recognition · 2023Article
- Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).Diagnostics (Basel, Switzerland) · 2023Article
- Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.Journal of mathematical imaging and vision · 2021Article
- PARTITIONING SIGNAL CLASSES USING TRANSPORT TRANSFORMS FOR DATA ANALYSIS AND MACHINE LEARNING.Sampling theory, signal processing, and data analysis · 2021Article
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8 authors.
Funding
Abstract
We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method - utilizing a nearest-subspace algorithm in the R-CDT space - is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at [1].
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