영상 범주 분류

Vision Transformer 또는 bag-of-visual-words 영상 분류기 생성

Vision Transformer 신경망을 사용하거나 영상을 표현한 시각 단어가 나타나는 횟수를 보여주는 히스토그램을 생성하여 영상을 범주로 분류합니다.

앱

영상 레이블 지정기	컴퓨터 비전 응용 분야에서 영상에 레이블 지정
비디오 레이블 지정기	Label video for computer vision applications

함수

모두 확장

ViT(Vision Transformer)

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

Bag-of-Features

`bagOfFeatures`	Bag-of-visual-words 객체
`trainImageCategoryClassifier`	Train an image category classifier
`imageCategoryClassifier`	Predict image category
`imageDatastore`	이미지 데이터의 데이터저장소

도움말 항목

시작하기

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.

영상 분류

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.

추천 예제

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.

라이브 스크립트 열기

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.

라이브 스크립트 열기

Image Category Classification Using Deep Learning

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

라이브 스크립트 열기

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.

라이브 스크립트 열기

Object Counting

Use morphological operations to count objects in a video stream.

라이브 스크립트 열기