plot_superpixels

0th

Percentile

Test super pixel segmentation

The segmentation of an image into superpixels are an important step in generating explanations for image models. It is both important that the segmentation is correct and follows meaningful patterns in the picture, but also that the size/number of superpixels are appropriate. If the important features in the image are chopped into too many segments the permutations will probably damage the picture beyond recognition in almost all cases leading to a poor or failing explanation model. As the size of the object of interest is varying it is impossible to set up hard rules for the number of superpixels to segment into - the larger the object is relative to the size of the image, the fewer superpixels should be generated. Using plot_superpixels it is possible to evaluate the superpixel parameters before starting the time consuming explanation function.

Usage
plot_superpixels(path, n_superpixels = 50, weight = 20, n_iter = 10,
  colour = "black")
Arguments
path

The path to the image. Must be readable by magick::image_read()

n_superpixels

The number of superpixels to segment into

weight

How high should locality be weighted compared to colour. High values leads to more compact superpixels, while low values follow the image structure more

n_iter

How many iterations should the segmentation run for

colour

What line colour should be used to show the segment boundaries

Value

A ggplot object

Aliases
  • plot_superpixels
Examples
# NOT RUN {
image <- system.file('extdata', 'produce.png', package = 'lime')

# plot with default settings
plot_superpixels(image)

# Test different settings
plot_superpixels(image, n_superpixels = 100, colour = 'white')

# }
Documentation reproduced from package lime, version 0.5.1, License: MIT + file LICENSE

Community examples

Looks like there are no examples yet.