# 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
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]])]
],