Summary of Learning Geometric Invariant Features For Classification Of Vector Polygons with Graph Message-passing Neural Network, by Zexian Huang et al.
Learning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network
by Zexian Huang, Kourosh Khoshelham, Martin Tomko
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed graph message-passing neural network (PolyMP) learns geometric-invariant features for shape classification of vector polygons. It achieves robust performances on benchmark datasets by combining a graph representation of polygons with permutation-invariant PolyMP. The model is invariant to geometric transformations and robust to trivial vertex removals, showing strong generalizability from synthetic glyphs to real-world building footprints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Polygon shape classification is important in spatial analysis. This study proposes a new approach using graph representations and message-passing neural networks (PolyMP) to learn geometric features. The method works well on different datasets and can handle different types of transformations. This means it’s useful for real-world applications like analyzing building footprints. |
Keywords
» Artificial intelligence » Classification » Neural network
