Summary of A Unified Model Selection Technique For Spectral Clustering Based Motion Segmentation, by Yuxiang Huang et al.
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation
by Yuxiang Huang, John Zelek
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Motion segmentation, a crucial problem in computer vision, has shown impressive results in robotics, autonomous driving, and action recognition. Spectral clustering methods have been effective, but they often require knowing the number of motions present, reducing their practicality. To address this limitation, we propose a unified model selection technique that combines existing techniques to automatically infer the number of motion groups for spectral clustering-based motion segmentation methods. Our method is evaluated on the KT3DMoSeg dataset and achieves competitive results compared to baselines where the number of clusters is given. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Motion segmentation is important for robots, self-driving cars, and recognizing actions. Spectral clustering helps group objects or movements together. But current methods need to know how many groups there are, which makes them less useful. We created a new way to figure out how many groups there are without needing that information. It works by combining different ways to pick the right model for motion segmentation. |
Keywords
» Artificial intelligence » Spectral clustering
