Research map / Learning theory and algorithms / Clustering
Superpixels and Polygons Using Simple Non-Iterative Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace SLIC's iterative clustering with priority-queue pixel assignment and online centroid updates | SNIC · Berkeley 300 | F-measure | Helped |
| Replace SLIC's iterative clustering with priority-queue pixel assignment and online centroid updates | SNIC · Berkeley 300 | boundary recall and precision | Helped |
| Enforce connectivity during clustering rather than as a post-processing step | SNIC · Berkeley 300 | memory consumption | Helped |
| Use SNIC superpixel boundaries as the basis for polygon generation | SNICPOLY · Berkeley 300 | CUSE | Helped |
| Use SNIC superpixel boundaries as the basis for polygon generation | SNICPOLY · Berkeley 300 | F-measure | Helped |
| Use SNIC superpixel boundaries as the basis for polygon generation | SNICPOLY · Berkeley 300 | runtime | Helped |
| Restrict pixel visits to candidates connected to a growing cluster | SNIC · Berkeley 300 | runtime | Helped |
Closest papers by meaning
- Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels (CVPR 2016 · similarity 0.90)
- Learning the Superpixel in a Non-Iterative and Lifelong Manner (CVPR 2021 · similarity 0.88)
- FLIC: Fast Linear Iterative Clustering With Active Search (AAAI 2018 · similarity 0.88)
- Hierarchical Histogram Threshold Segmentation - Auto-terminating High-detail Oversegmentation (CVPR 2024 · similarity 0.83)
- SCCS: Deep Neural Spectral Clustering for Self-Supervised Subcellular Structure Segmentation (AAAI 2025 · similarity 0.83)
- SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection (AAAI 2024 · similarity 0.82)
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