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classifyRegions

(Not recommended) Classify objects in image regions using Fast R-CNN object detector

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The classifyRegions function and Fast R-CNN object detectors are not recommended. Use a different type of object detector instead. For more information, see Version History.

Syntax

[labels,scores] = classifyRegions(detector,I,rois)
[labels,scores,allScores] = classifyRegions(detector,I,rois)
[___] = classifyRegions(___,'ExecutionEnvironment',resource)

Description

[labels,scores] = classifyRegions(detector,I,rois) classifies objects within the regions of interest of image I, using a Fast R-CNN (regions with convolutional neural networks) object detector. For each region, classifyRegions returns the class label with the corresponding highest classification score.

When using this function, use of a CUDA® enabled NVIDIA® GPU is highly recommended. The GPU reduces computation time significantly. Usage of the GPU requires Parallel Computing Toolbox™. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).

example

[labels,scores,allScores] = classifyRegions(detector,I,rois) also returns all the classification scores of each region. The scores are returned in an M-by-N matrix of M regions and N class labels.

[___] = classifyRegions(___,'ExecutionEnvironment',resource) specifies the hardware resource used to classify object within image regions: "auto", "cpu", or "gpu". You can use this syntax with either of the preceding syntaxes.

Examples

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Open Live Script

Configure a Fast R-CNN object detector and use it to classify objects within multiple regions of an image.

Load a fastRCNNObjectDetector object that is pretrained to detect stop signs.

data = load('rcnnStopSigns.mat','fastRCNN');
fastRCNN = data.fastRCNN;

Read in a test image containing a stop sign.

I = imread('stopSignTest.jpg');
figure
imshow(I)

Figure contains an axes object. The hidden axes object contains an object of type image.

Specify regions of interest to classify within the test image.

rois = [416   143    33    27
        347   168    36    54];

Classify the image regions and inspect the output labels and classification scores. The labels come from the ClassNames property of the detector.

[labels,scores] = classifyRegions(fastRCNN,I,rois)
labels = 2x1 categorical
     stopSign 
     Background 


scores = 2x1 single column vector

    0.9969
    1.0000

The detector has high confidence in the classifications. Display the classified regions on the test image.

detectedI = insertObjectAnnotation(I,'rectangle',rois,cellstr(labels));
 
figure
imshow(detectedI)

Figure contains an axes object. The hidden axes object contains an object of type image.

Input Arguments

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Fast R-CNN object detector, specified as a fastRCNNObjectDetector object. To create this object, call the trainFastRCNNObjectDetector function with training data as input.

Input image, specified as a real, nonsparse, grayscale or RGB image.

The detector is sensitive to the range of the input image. Therefore, ensure that the input image range is similar to the range of the images used to train the detector. For example, if the detector was trained on uint8 images, rescale this input image to the range [0, 255] by using the im2uint8 or rescale function. The size of this input image should be comparable to the sizes of the images used in training. If these sizes are very different, the detector has difficulty detecting objects because the scale of the objects in the input image differs from the scale of the objects the detector was trained to identify. Consider whether you used the SmallestImageDimension property during training to modify the size of training images.

Data Types: uint8 | uint16 | int16 | double | single | logical

Regions of interest within the image, specified as an M-by-4 matrix defining M rectangular regions. Each row contains a four-element vector of the form [x y width height]. This vector specifies the upper left corner and size of a region in pixels.

Hardware resource used to classify image regions, specified as "auto", "gpu", or "cpu".

  • "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.

Example: classifyRegions(___,'ExecutionEnvironment',"cpu")

Output Arguments

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Classification labels of regions, returned as an M-by-1 categorical array. M is the number of regions of interest in rois. Each class name in labels corresponds to a classification score in scores and a region of interest in rois. classifyRegions obtains the class names from the input detector.

Highest classification score per region, returned as an M-by-1 vector of values in the range [0, 1]. M is the number of regions of interest in rois. Each classification score in scores corresponds to a class name in labels and a region of interest in rois. A higher score indicates higher confidence in the classification.

All classification scores per region, returned as an M-by-N matrix of values in the range [0, 1]. M is the number of regions in rois. N is the number of class names stored in the input detector. Each row of classification scores in allscores corresponds to a region of interest in rois. A higher score indicates higher confidence in the classification.

Version History

Introduced in R2017a

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R2024b: Not Recommended

Starting in R2024b, R-CNN object detectors are no longer recommended. Instead, use a different type of object detector, such as a yoloxObjectDetector or yolov4ObjectDetector object. These object detectors are faster than R-CNN object detectors. For more information, see Choose an Object Detector.

See Also

Apps

Functions

Objects