Bilateral Space Video Segmentation
In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatio-temporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.
PDF Abstract
Datasets
DAVIS
DAVIS 2016
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | BVS | Jaccard (Mean) | 60.0 | # 76 | |
| Jaccard (Recall) | 66.9 | # 34 | ||||
| Jaccard (Decay) | 28.9 | # 1 | ||||
| F-measure (Mean) | 58.8 | # 75 | ||||
| F-measure (Recall) | 67.9 | # 32 | ||||
| F-measure (Decay) | 21.3 | # 3 | ||||
| J&F | 59.4 | # 76 |
