classify

Classify image as normal or anomalous

Syntax

isAnomaly = classify(detector,I)

isAnomaly = classify(detector,I,Name,Value)

Description

isAnomaly = classify(detector,I) predicts whether test image I is anomalous or normal, given an FCDD anomaly detector with an anomaly threshold.

isAnomaly = classify(detector,I,Name,Value) specifies options using one or more name-value arguments. For example, classify(detector,I,MiniBatchSize=32) limits the batch size to 32.

Note

This function requires the Computer Vision Toolbox™ Automated Visual Inspection Library. You can install the Computer Vision Toolbox Automated Visual Inspection Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons. To run this function, you will require the Deep Learning Toolbox™.

Examples

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Calculate Metrics for Trained FCDD Anomaly Detector

This example uses:

Open Live Script

Load test images and corresponding labels, then create a datastore that reads the test data. The data set consists of grayscale images of handwritten digits 0–9.

[Xtest,catLabels] = digitTest4DArrayData;
dsTest = arrayDatastore(Xtest,IterationDimension=4);

Load a pretrained FCDD anomaly detector. This detector has been trained to classify the digit 8 as normal and all other digits as anomalies. Therefore, specify the set of positive labels as the set of digits between 0 and 9, excluding 8.

load("digit8AnomalyDetector.mat");
posLabels = setdiff(string(0:9),"8");

Classify each test image as normal or anomalous.

labelsPred = classify(detector,dsTest);

Calculate metrics for the anomaly detection results.

metrics = evaluateAnomalyDetection(labelsPred,catLabels,posLabels)

Evaluating anomaly detection results
------------------------------------
* Finalizing... Done.
* Data set metrics:

    GlobalAccuracy    MeanAccuracy    Precision    Recall    Specificity    F1Score    FalsePositiveRate    FalseNegativeRate
    ______________    ____________    _________    ______    ___________    _______    _________________    _________________

        0.7662           0.839         0.98971     0.748        0.93        0.85204          0.07                 0.252

metrics = 
  anomalyDetectionMetrics with properties:

              ConfusionMatrix: [2x2 table]
    NormalizedConfusionMatrix: [2x2 table]
               DataSetMetrics: [1x8 table]
                 ClassMetrics: [2x2 table]

Display the confusion matrix.

cm = metrics.ConfusionMatrix

cm=2×2 table
               Normal    Anomaly
               ______    _______

    Normal       465        35  
    Anomaly     1134      3366

Input Arguments

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`detector` — FCDD anomaly detector
`fcddAnomalyDetector` object

FCDD anomaly detector, specified as a fcddAnomalyDetector object. You must set the Threshold property of the detector before calling this function.

`I` — Test image
M-by-N-by-3 numeric array | M-by-N numeric matrix | formatted `dlarray` object | datastore

Test image, specified in one of these formats:

M-by-N-by-3 numeric array representing a truecolor image
M-by-N numeric matrix representing a grayscale image
Formatted dlarray (Deep Learning Toolbox) object with two spatial dimensions. You can specify multiple test images by including a batch dimension.
Datastore that reads and returns M-by-N-by-3 truecolor images or M-by-N grayscale images.

Name-Value Arguments

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: isAnomaly = classify(detector,I,MiniBatchSize=32) limits the batch size to 32.

`MiniBatchSize` — Size of batches
`128` (default) | positive integer

Size of batches for calculating predictions, specified as a positive integer. Larger batch sizes lead to faster processing but take up more memory.

`ExecutionEnvironment` — Hardware resource
`"auto"` (default) | `"gpu"` | `"cpu"`

Hardware resource on which to run the detector, specified as "auto", "gpu", or "cpu". The table shows the valid hardware resource values.

Resource	Action
`"auto"`	Use a GPU if it is available. Otherwise, use the CPU.
`"gpu"`	Use the GPU. To use a GPU, you must have Parallel Computing Toolbox™ and a CUDA^® enabled NVIDIA^® GPU. If a suitable GPU is not available, the function returns an error. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).
`"cpu"`	Use the CPU.