Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search Article Swipe
Guangyu Meng
,
Ruyu Zhou
,
Liu Liu
,
Peixian Liang
,
Fang Liu
,
Danny Chen
,
Michael Niemier
,
Xiaobo Sharon Hu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.07378
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.07378
Earth Mover's Distance (EMD) is an important similarity measure between two distributions, used in computer vision and many other application domains. However, its exact calculation is computationally and memory intensive, which hinders its scalability and applicability for large-scale problems. Various approximate EMD algorithms have been proposed to reduce computational costs, but they suffer lower accuracy and may require additional memory usage or manual parameter tuning. In this paper, we present a novel approach, NNS-EMD, to approximate EMD using Nearest Neighbor Search (NNS), in order to achieve high accuracy, low time complexity, and high memory efficiency. The NNS operation reduces the number of data points compared in each NNS iteration and offers opportunities for parallel processing. We further accelerate NNS-EMD via vectorization on GPU, which is especially beneficial for large datasets. We compare NNS-EMD with both the exact EMD and state-of-the-art approximate EMD algorithms on image classification and retrieval tasks. We also apply NNS-EMD to calculate transport mapping and realize color transfer between images. NNS-EMD can be 44x to 135x faster than the exact EMD implementation, and achieves superior accuracy, speedup, and memory efficiency over existing approximate EMD methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.07378
- https://arxiv.org/pdf/2401.07378
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390962192
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390962192Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.07378Digital Object Identifier
- Title
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Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor SearchWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-14Full publication date if available
- Authors
-
Guangyu Meng, Ruyu Zhou, Liu Liu, Peixian Liang, Fang Liu, Danny Chen, Michael Niemier, Xiaobo Sharon HuList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.07378Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.07378Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.07378Direct OA link when available
- Concepts
-
Earth mover's distance, Speedup, Computer science, Scalability, k-nearest neighbors algorithm, Nearest neighbor search, Similarity (geometry), Algorithm, Artificial intelligence, Pattern recognition (psychology), Image (mathematics), Parallel computing, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.NNS-EMD, | 73 |
| abstract_inverted_index.Neighbor | 79 |
| abstract_inverted_index.accuracy | 54 |
| abstract_inverted_index.achieves | 176 |
| abstract_inverted_index.compared | 104 |
| abstract_inverted_index.computer | 14 |
| abstract_inverted_index.domains. | 20 |
| abstract_inverted_index.existing | 184 |
| abstract_inverted_index.methods. | 187 |
| abstract_inverted_index.parallel | 113 |
| abstract_inverted_index.proposed | 45 |
| abstract_inverted_index.speedup, | 179 |
| abstract_inverted_index.superior | 177 |
| abstract_inverted_index.transfer | 160 |
| abstract_inverted_index.accuracy, | 87, 178 |
| abstract_inverted_index.approach, | 72 |
| abstract_inverted_index.calculate | 154 |
| abstract_inverted_index.datasets. | 129 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.iteration | 108 |
| abstract_inverted_index.operation | 97 |
| abstract_inverted_index.parameter | 63 |
| abstract_inverted_index.problems. | 38 |
| abstract_inverted_index.retrieval | 147 |
| abstract_inverted_index.transport | 155 |
| abstract_inverted_index.accelerate | 117 |
| abstract_inverted_index.additional | 58 |
| abstract_inverted_index.algorithms | 42, 142 |
| abstract_inverted_index.beneficial | 126 |
| abstract_inverted_index.efficiency | 182 |
| abstract_inverted_index.especially | 125 |
| abstract_inverted_index.intensive, | 29 |
| abstract_inverted_index.similarity | 7 |
| abstract_inverted_index.application | 19 |
| abstract_inverted_index.approximate | 40, 75, 140, 185 |
| abstract_inverted_index.calculation | 24 |
| abstract_inverted_index.complexity, | 90 |
| abstract_inverted_index.efficiency. | 94 |
| abstract_inverted_index.large-scale | 37 |
| abstract_inverted_index.processing. | 114 |
| abstract_inverted_index.scalability | 33 |
| abstract_inverted_index.applicability | 35 |
| abstract_inverted_index.computational | 48 |
| abstract_inverted_index.opportunities | 111 |
| abstract_inverted_index.vectorization | 120 |
| abstract_inverted_index.classification | 145 |
| abstract_inverted_index.distributions, | 11 |
| abstract_inverted_index.computationally | 26 |
| abstract_inverted_index.implementation, | 174 |
| abstract_inverted_index.state-of-the-art | 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |