Image Category Classification

Create vision transformer or bag of visual words image classifier

Classify images into categories, using vision transformer neural networks or by generating a histogram of visual word occurrences that represent images.

Apps

Image Labeler	Label images for computer vision applications
Video Labeler	Label video for computer vision applications

Functions

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ViT (Vision Transformer)

`visionTransformer`	Pretrained vision transformer (ViT) neural network (Since R2023b)
`patchEmbeddingLayer`	Patch embedding layer (Since R2023b)

Bag of Features

`bagOfFeatures`	Bag of visual words object
`trainImageCategoryClassifier`	Train an image category classifier
`imageCategoryClassifier`	Predict image category
`imageDatastore`	Datastore for image data

Topics

Get Started

Get Started with the Image Labeler
Interactively label rectangular ROIs for object detection, pixels for semantic segmentation, polygons for instance segmentation, and scenes for image classification.
Get Started with the Video Labeler
Interactively label rectangular ROIs for object detection, pixels for semantic segmentation, polygons for instance segmentation, and scenes for image classification in a video or image sequence.

Classify Images

Create a Custom Feature Extractor
You can use the bag-of-features (BoF) framework with many different types of image features.
Image Classification with Bag of Visual Words
Use the Computer Vision Toolbox™ functions for image category classification by creating a bag of visual words.

Featured Examples

Train Vision Transformer Network for Image Classification

Fine-tune a pretrained vision transformer (ViT) neural network neural network to perform classification on a new collection of images.

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Image Category Classification Using Bag of Features

Use a bag of features approach for image category classification. This technique is also often referred to as bag of words. Visual image categorization is a process of assigning a category label to an image under test. Categories may contain images representing just about anything, for example, dogs, cats, trains, boats.

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Image Category Classification Using Deep Learning

Use a pretrained Convolutional Neural Network (CNN) as a feature extractor for training an image category classifier.

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Cell Counting

Use a combination of basic morphological operators and blob analysis to extract information from a video stream. In this case, the example counts the number of E. Coli bacteria in each video frame. Note that the cells are of varying brightness, which makes the task of segmentation more challenging.

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Object Counting

Use morphological operations to count objects in a video stream.

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