# When an AI tool "rotos" a subject, is the result a segmentation mask or an alpha matte, and which metrics (J&F versus MAD/MSE/Grad/Conn/dtSSD) apply to each?

- id: art_01M5NNY4YNRPHB2KGYV3BR4E0B
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T11, T20 · tags: none
- updated: 2026-10-24T00:45:55.536Z
- url: https://veyrul.com/articles/art_01M5NNY4YNRPHB2KGYV3BR4E0B

Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.

Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core.

Curator qualifications: Mask-vs-matte distinction and metric families are consistent with the sources read; the concept is well supported.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2408.00714v2",
      "note": "Table 6; limitations; metrics"
    },
    {
      "url": "https://arxiv.org/abs/1704.00675v3",
      "note": "Table 6; limitations; metrics"
    },
    {
      "url": "https://arxiv.org/abs/2108.11515v1",
      "note": "Table 6; limitations; metrics"
    },
    {
      "url": "https://arxiv.org/abs/2501.14677v2",
      "note": "Table 6; limitations; metrics"
    },
    {
      "url": "https://github.com/pq-yang/MatAnyone/blob/e5ddc534c1fff9bb9e54cf476095d29071b7cb4f/README.md",
      "note": "Table 6; limitations; metrics"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Video-object-segmentation models such as SAM 2 predict binary object masks and are scored with J&F, which in the DAVIS protocol is a region IoU plus a boundary F-measure on binary masks. Matting models such as RVM and MatAnyone predict a per-pixel alpha under I = alpha*F + (1-alpha)*B and are scored with MAD, MSE, Grad, Conn and dtSSD. In the pinned SAM 2 paper text the focus is objects with a clearly defined boundary and the words alpha and hair do not occur, so its J&F numbers do not measure fractional-alpha edges such as hair. MatAnyone splits a frame into a core (alpha 0 or 1) and a boundary (alpha between 0 and 1) and, lacking ground-truth alpha on real video, scores real clips only on the core. · locator: Table 6; limitations; metrics · source: https://arxiv.org/abs/2408.00714v2

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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