confusionMatrix

Compute confusion matrix of instance segmentation results

Since R2024b

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Syntax

[confusionMatrix,confusionClassNames] = confusionMatrix(metrics)
[confusionMatrix,confusionClassNames] = confusionMatrix(metrics,Name=Value)

Description

[confusionMatrix,confusionClassNames] = confusionMatrix(metrics) computes the confusion matrix for all overlap thresholds in the OverlapThreshold property of metrics, and returns the confusion matrix and the class names. The function considers all segmentations to compute the confusion matrix.

[confusionMatrix,confusionClassNames] = confusionMatrix(metrics,Name=Value) specifies one or more name-value arguments. For example, ScoreThreshold=0.3 specifies to disregard segmentations with a confidence score value below 0.3 when computing the confusion matrix.

Input Arguments

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Instance segmentation performance metrics, specified as an instanceSegmentationMetrics object.

Name-Value Arguments

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Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: confusionMatrix(metrics,ScoreThreshold=0.3) specifies segmentations with a confidence score value below 0.3 to be disregarded when computing the confusion matrix.

Confidence score threshold values to use for computing the confusion matrix, specified as a numeric scalar in the range [0, 1] or a numeric vector of values in the range [0, 1]. The function filters out predictions with confidence scores less than the threshold value when evaluating the confusion matrix.

Increase this value to reduce the number of false positives, at the possible expense of missing some true positives.

Overlap threshold values to use for computing the confusion matrix, specified as a numeric scalar or numeric vector. Each overlap threshold value must be an element of the OverlapThreshold property of the instanceSegmentationMetrics object. By default, the function returns the confusion matrix computed at all the overlap thresholds specified by the OverlapThreshold property.

Confusion matrix normalization option, specified as a numeric or logical 0 (or false) or 1 (or true). If you specify Normalize as true, the function normalizes the elements of the confusion matrix in the confMat argument by the number of objects known to belong to each class. For each overlap threshold, each element (i, j) in the normalized confusion matrix is the count of objects known to belong to class i, but predicted to belong to class j, divided by the total number of objects predicted in class j.

Output Arguments

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Confusion matrix, returned as an M-by-N cell array. M is the number of confidence score thresholds specified by the ScoreThreshold argument, and N is the number of overlap thresholds specified by the OverlapThreshold argument.

Each element of the confMat cell array is a square numeric matrix of size (C+1)-by-(C+1), where C is the number of classes. Each element (i, j) of the matrix is the count of matched bounding boxes predicted to belong to class j, but that have a ground truth annotation class of i.

Row and column C+1 in the confusion matrix correspond to these unmatched conditions:

  • Column C+1 — Undetected objects, or false negative predictions. Each element in this column is the number of ground truth annotations of the corresponding class unmatched with any predicted bounding box.

  • Row C+1 — Incorrect predictions, or false positive predictions. Each element in this row is the number of predicted bounding boxes of the corresponding class unmatched with any ground truth annotation.

Class names corresponding to the returned confusion matrices, returned as a string array of size C+1. The elements of the string array correspond to the classes stored in the ClassNames property of the instanceSegmentationMetrics object metrics and an extra background class. The background class, "background", corresponds to the row and column C+1 of the confusion matrix, which count the false positives and false negatives.

Version History

Introduced in R2024b

See Also

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