SegmentationMajorityVote

crowdkit.aggregation.image_segmentation.segmentation_majority_vote.SegmentationMajorityVote | Source code

SegmentationMajorityVote(
    self,
    on_missing_skill: str = 'error',
    default_skill: Optional[float] = None
)

Segmentation Majority Vote - chooses a pixel if more than half of workers voted.

This method implements a straightforward approach to the image segmentations aggregation: it assumes that if pixel is not inside in the worker's segmentation, this vote counts as 0, otherwise, as 1. Next, the SegmentationEM aggregates these categorical values for each pixel by the Majority Vote.

The method also supports weighted majority voting if skills were provided to fit method.

Doris Jung-Lin Lee. 2018. Quality Evaluation Methods for Crowdsourced Image Segmentation https://ilpubs.stanford.edu:8090/1161/1/main.pdf

Parameters Description

Parameters Type Description

Parameters	Type	Description
`default_skill`	Optional[float]	A default skill value for missing skills.
`segmentations_`	Series	Tasks' segmentations. A pandas.Series indexed by `task` such that `labels.loc[task]` is the tasks's aggregated segmentation.
`on_missing_skill`	str	How to handle assignments done by workers with unknown skill. Possible values: "error" — raise an exception if there is at least one assignment done by user with unknown skill; "ignore" — drop assignments with unknown skill values during prediction. Raise an exception if there is no assignments with known skill for any task; value — default value will be used if skill is missing.

default_skill

Optional[float]

A default skill value for missing skills.

segmentations_

Series

Tasks' segmentations. A pandas.Series indexed by task such that labels.loc[task] is the tasks's aggregated segmentation.

on_missing_skill

str

How to handle assignments done by workers with unknown skill. Possible values:

"error" — raise an exception if there is at least one assignment done by user with unknown skill;
"ignore" — drop assignments with unknown skill values during prediction. Raise an exception if there is no assignments with known skill for any task;
value — default value will be used if skill is missing.

Examples:

import numpy as np
import pandas as pd
from crowdkit.aggregation import SegmentationMajorityVote
df = pd.DataFrame(
    [
        ['t1', 'p1', np.array([[1, 0], [1, 1]])],
        ['t1', 'p2', np.array([[0, 1], [1, 1]])],
        ['t1', 'p3', np.array([[0, 1], [1, 1]])]
    ],
    columns=['task', 'worker', 'segmentation']
)
result = SegmentationMajorityVote().fit_predict(df)

Methods Summary

Method	Description
fit	Fit the model.
fit_predict	Fit the model and return the aggregated segmentations.

Crowd-Kit

Overview

Reference

Aggregation

Datasets

Learning

Metrics

Postprocessing