What is Image Annotation?
Computer vision, the technology that allows computers to obtain high-level knowledge from digital images or videos and comprehend and interpret visual information like humans, relies heavily on image annotation.
The process of creating most computer vision models begins with image annotation. For computer vision, datasets are required as useful components of machine learning and deep learning.
Types of Image Annotation
For image classification, object identification, object recognition, picture segmentation, machine learning, and computer vision models, image annotation is commonly employed. It is a strategy for creating dependable datasets for machine learning models to train on, and it is effective for both supervised and semi-supervised models.
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Image Classification
Image classification is a sort of machine learning model in which the complete image is identified by a single label. The goal of the image annotation process for image classification models is to detect the existence of comparable items in the dataset's images.
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Object Detection & Object Recognition
Object detection or recognition models go one step farther than image categorization to determine the presence, location, and quantity of objects in a picture. The image annotation procedure requires drawing boundaries around every detected object in each image for this type of model, allowing us to locate the exact position and number of objects present in an image.
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Image Segmentation
Image segmentation is a sort of picture annotation in which an image is divided into several segments. In images, image segmentation is used to find objects and boundaries (lines, curves, and so on). It works at the pixel level, assigning a specific object or class to each pixel in a picture. It's employed in applications that require improved input classification accuracy.
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Boundary Recognition
Lines or borders of items inside a picture are identified using this type of image annotation. The edges of a specific object or parts of topography in the image can serve as boundaries.