ArticleInternational journal of computer vision2013
A linear optimal transportation framework for quantifying and visualizing variations in sets of images.
Article in International journal of computer vision, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data.... International Conference on Learning Representations · 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
- Discovering the gene-brain-behavior link in autism via generative machine learning.Science advances · 2024Article
- 3D imaging of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography enables drug testing.Scientific reports · 2024Article
- Linear optimal transport subspaces for point set classification.Research square · 2024Article
- A Wasserstein-Type Distance for Gaussian Mixtures on Vector Bundles with Applications to Shape Analysis.SIAM journal on imaging sciences · 2024Article
- A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves.Frontiers in neuroscience · 2023Article
- Three-dimensional virtual histology of the human hippocampus based on phase-contrast computed tomography.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- 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
- Investigating impact of cardiorespiratory fitness in reducing brain tissue loss caused by ageing.Brain communications · 2021Article
- Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Hyperbolic Wasserstein Distance for Shape Indexing.IEEE transactions on pattern analysis and machine intelligence · 2020Article
- Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions.Journal of thyroid research · 2020Article
- Assessing Postconcussive Reaction Time Using Transport-Based Morphometry of Diffusion Tensor Images.AJNR. American journal of neuroradiology · 2019Article
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5 authors.
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Abstract
Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of 'mass' that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover's distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.
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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.