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ImageCases[image]

gives an association of lists of subimages for each identified category of objects in image.

ImageCases[image,category]

gives a list of subimages identified as an instance of the specified category.

ImageCases[image,categoryprop]

gives the specified property prop for each identified subimage.

ImageCases[image,{category1,category2,…}]

gives an association with lists of subimages identified as being instances of each of the categoryi.

ImageCases[video,…]

gives a time series of detected objects in frames of video.

Details and Options
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Data  
Categories  
Properties  
Options  
AcceptanceThreshold  
MaxFeatures  
MaxOverlapFraction  
TargetDevice  
Applications  
See Also
Related Guides
Related Links
History
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ImageCases

ImageCases[image]

gives an association of lists of subimages for each identified category of objects in image.

ImageCases[image,category]

gives a list of subimages identified as an instance of the specified category.

ImageCases[image,categoryprop]

gives the specified property prop for each identified subimage.

ImageCases[image,{category1,category2,…}]

gives an association with lists of subimages identified as being instances of each of the categoryi.

ImageCases[video,…]

gives a time series of detected objects in frames of video.

Details and Options

  • ImageCases is also known as object detection and attempts to find instances of an object category present in an image and returns a list of subimages per category.
  • Possible forms for category include:
  • "concept"named concept, as used in "Concept" entities
    "word"English word, as used in WordData
    wordspecword sense specification, as used in WordData
    Entity[…]any appropriate entity
    category1|category2|…any of the categoryi
  • The property prop can be any of the following:
  • "BoundingBox"the bounding box given as a Rectangle
    "Confidence"confidence of the identification
    "Dimensions"width and height of the subimage
    "Image"the identified subimage (default)
    "Position"center of the identified bounding box
    {prop1,prop2,…}a list of properties
  • Masked subimage properties:
  • "Mask"binary mask indicating the object
    "MaskBoundingBox"the bounding box of the masked image
    "MaskCentroid"centroid of the mask
    "MaskedImage"subimage masked by the component shape
    "MaskMedoid"medoid of the mask
    "MaskPosition"center of the mask bounding box
  • The following options can be given:
  • AcceptanceThreshold Automaticidentification acceptance threshold
    MaxFeatures Automaticmaximum number of subimages to return
    MaxOverlapFraction Automaticmaximum bounding box overlap
    PerformanceGoal"Balanced"aspects of performance to try to optimize
    TargetDevice "CPU"the target device on which to compute
  • ImageCases uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.
  • ImageCases may download resources that will be stored in your local object store at $LocalBase, and can be listed using LocalObjects[] and removed using ResourceRemove.

Examples

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Basic Examples  (3)

Find identified subimages of an image:

Find subimages identified as a dog:

Get the detected and segmented objects of an image:

Scope  (11)

Data  (2)

Detect objects in an image:

Detect objects in frames of video:

Objects at a given time:

Categories  (2)

Find all the detectable objects in an image:

Find all birds in the image:

Properties  (7)

Find the birds' bounding boxes:

Return multiple properties:

Query properties of the concept:

Group together the detection for multiple categories:

Return the specified properties for all the detectable categories:

Get the detected and segmented objects of an image:

Mask of each detected subimage:

Options  (4)

AcceptanceThreshold  (1)

Objects with low probability are not returned:

Allowing a lower probability may result in more objects being recognized:

MaxFeatures  (1)

By default, all the detected objects are returned:

Specify a maximum number of results:

MaxOverlapFraction  (1)

The detected bounding boxes may overlap each other:

Find only non-intersecting objects:

TargetDevice  (1)

By default, the function is evaluated on CPU:

Use the TargetDevice option to specify a different device:

Applications  (1)

Build an image captioning function using the counts of detected objects:

Transform the object counts in a string:

Package all the code in a function to automatically generate captions:

Try the function on a different image:

Caption an image with no identified objects:

Wolfram Research (2019), ImageCases, Wolfram Language function, https://reference.wolfram.com/language/ref/ImageCases.html (updated 2025).

Text

Wolfram Research (2019), ImageCases, Wolfram Language function, https://reference.wolfram.com/language/ref/ImageCases.html (updated 2025).

CMS

Wolfram Language. 2019. "ImageCases." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/ImageCases.html.

APA

Wolfram Language. (2019). ImageCases. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ImageCases.html

BibTeX

@misc{reference.wolfram_2025_imagecases, author="Wolfram Research", title="{ImageCases}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/ImageCases.html}", note=[Accessed: 04-February-2026]}

BibLaTeX

@online{reference.wolfram_2025_imagecases, organization={Wolfram Research}, title={ImageCases}, year={2025}, url={https://reference.wolfram.com/language/ref/ImageCases.html}, note=[Accessed: 04-February-2026]}