R: Test super pixel segmentation

plot_superpixels {lime} | R Documentation

## Test super pixel segmentation

### Description

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

### Examples

    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')

[Package lime version 0.5.1 Index]