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Bildkategorie-Klassifizierung

Erstellen eines Vision-Transformators oder Bag-of-Visual-Words-Bildklassifikators

Mit neuronalen Vision-Transformator-Netzwerken oder durch Generierung eines Histogramms der Visual-Word-Instanzen, die Bilder darstellen, können Sie Bilder in Kategorien klassifizieren.

Apps

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

Funktionen

alle erweitern

ViT (Vision Transformer)

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

Themen

Erste Schritte

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.

Klassifizieren von Bildern

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.

Enthaltene Beispiele

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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