semanticSegmentationMetrics

Semantic segmentation quality metrics

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Description

A semanticSegmentationMetrics object encapsulates semantic segmentation quality metrics for a set of images.

Creation

Create a semanticSegmentationMetrics object using the evaluateSemanticSegmentation function.

Properties

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This property is read-only.

Confusion matrix, specified as a square table. Each table element (i,j) is the count of pixels known to belong to class i but predicted to belong to class j.

This property is read-only.

Normalized confusion matrix, specified as a square table. Each table element (i,j) is the count of pixels known to belong to class i but predicted to belong to class j, divided by the total number of pixels predicted in class j. Elements are in the range [0, 1].

This property is read-only.

Semantic segmentation metrics aggregated over the data set, specified as a table. DataSetMetrics contains up to five metrics, depending on the value of the 'Metrics' name-value pair used with evaluateSemanticSegmentation:

  • GlobalAccuracy — Ratio of correctly classified pixels to total pixels, regardless of class.

  • MeanAccuracy — Ratio of correctly classified pixels in each class to total pixels, averaged over all classes. The value is equal to the mean of ClassMetrics.Accuracy.

  • MeanIoU — Average intersection over union (IoU) of all classes. The value is equal to the mean of ClassMetrics.IoU.

  • WeightedIoU — Average IoU of all classes, weighted by the number of pixels in the class.

  • MeanBFScore — Average boundary F1 (BF) score of all images. The value is equal to the mean of ImageMetrics.BFScore.

This property is read-only.

Semantic segmentation metrics for each class, specified as a table. ClassMetrics contains up to three metrics for each class, depending on the value of the 'Metrics' name-value pair used with evaluateSemanticSegmentation:

  • Accuracy — Ratio of correctly classified pixels in each class to the total number of pixels belonging to that class according to the ground truth. Accuracy can be expressed as:

    Accuracy = (TP + TN ) / (TP + TN + FP + FN)

     PositiveNegative
    PositiveTP: True PositiveFN: False Negative
    NegativeFP: False PositiveTN: True Negative

    TP: True positives and FN is the number of false negatives.

  • IoU — Ratio of correctly classified pixels to the total number of pixels that are assigned that class by the ground truth and the predictor. IoU can be expressed as:

    IoU = TP / (TP + FP + FN)

    The image describes the true positives (TP), false positives (FP), and false negatives (FN).

  • MeanBFScore — Boundary F1 score for each class, averaged over all images.

This property is read-only.

Semantic segmentation metrics for each image in the data set, specified as a table. ImageMetrics contains up to five metrics, depending on the value of the 'Metrics' name-value pair used with evaluateSemanticSegmentation:

  • GlobalAccuracy — Ratio of correctly classified pixels to total pixels, regardless of class

  • MeanAccuracy — Ratio of correctly classified pixels to total pixels, averaged over all classes in the image

  • MeanIoU — Average IoU of all classes in the image

  • WeightedIoU — Average IoU of all classes in the image, weighted by the number of pixels in each class

  • MeanBFScore — Average BF score of each class in the image

Each image metric returns a vector, with one element for each image in the data set. The order of the rows matches the order of the images defined by the input PixelLabelDatastore objects representing the data set.

Examples

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This example uses:

Open Live Script

The triangleImages data set has 100 test images with ground truth labels. Define the location of the data set.

dataSetDir = fullfile(toolboxdir('vision'),'visiondata','triangleImages');

Define the location of the test images.

testImagesDir = fullfile(dataSetDir,'testImages');

Define the location of the ground truth labels.

testLabelsDir = fullfile(dataSetDir,'testLabels');

Create an imageDatastore holding the test images.

imds = imageDatastore(testImagesDir);

Define the class names and their associated label IDs.

classNames = ["triangle","background"];
labelIDs   = [255 0];

Create a pixelLabelDatastore holding the ground truth pixel labels for the test images.

pxdsTruth = pixelLabelDatastore(testLabelsDir,classNames,labelIDs);

Load a semantic segmentation network that has been trained on the training images of noisyShapes.

net = load('triangleSegmentationNetwork');
net = net.net;

Run the network on the test images. Predicted labels are written to disk in a temporary directory and returned as a pixelLabelDatastore.

pxdsResults = semanticseg(imds,net,"WriteLocation",tempdir);
Running semantic segmentation network
-------------------------------------
* Processed 100 images.

Evaluate the prediction results against the ground truth.

metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth);
Evaluating semantic segmentation results
----------------------------------------
* Selected metrics: global accuracy, class accuracy, IoU, weighted IoU, BF score.
* Processed 100 images.
* Finalizing... Done.
* Data set metrics:

    GlobalAccuracy    MeanAccuracy    MeanIoU    WeightedIoU    MeanBFScore
    ______________    ____________    _______    ___________    ___________

       0.90624          0.95085       0.61588      0.87529        0.40652

Display the classification accuracy, the intersection over union, and the boundary F-1 score for each class.

metrics.ClassMetrics
ans=2×3 table
                  Accuracy      IoU      MeanBFScore
                  ________    _______    ___________

    triangle            1     0.33005     0.028664  
    background     0.9017      0.9017      0.78438

See Also

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Topics

Introduced in R2017b

Computer Vision Toolbox Documentation
Support