The process of categorizing photos in a dataset is required to practice an ML model is known as an image annotation. As a result, picture annotation is utilized to indicate the aspects your system needs to recognize. Supervised Learning is the process of developing an ML model given labeled data.

The annotation job is generally done by hand, with occasional assistance from a computer. The labels, known as “classes,” are predetermined by a Machine Learning engineer, who then feeds the graphics data to the machine vision model. After the training is done and deployed, it can anticipate and detect preset characteristics in fresh photos that have not yet been annotated.

  • COCO Dataset and Google’s OID are two popular annotated picture databases

Annotation techniques

Depending on the approach, several annotation shapes are employed to annotate a picture in AI image annotation. Annotation techniques such as landmarking and lines may also be utilized for picture annotation in addition to shapes.

The following are some of the most common ways to perform image annotation, which are utilized depending on the use case.

  • Polygon counts are used for marking irregular objects in a photograph. This is used to annotate the edges and identify each of the vertexes of the target item.
  • Landmarking– This is used to locate the most important points of interest in a photograph. Landmarks or significant points are terms used to describe such sites. Face recognition relies heavily on landmarking.
  • Lines and splines add slightly curved lines to the picture. This is important for annotating sidewalks, road signs, and other boundary indications using boundary recognition.
  • Bounding Boxes– In computer vision, this is the most frequent annotation shape. Rectangular rectangles often used to define the position of an item within an image are known as bounding boxes. They might be 2D or 3D.

Image Annotation applications

For image classification, object identification, object recognition, picture segmentation, 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 beneficial for both supervised and semi-supervised models.

Image segmentation

It’s a sort of picture annotation in which an image is divided into many parts. In pictures, image segmentation is used to find objects and borders. It’s done pixel by pixel, assigning each pixel in a picture to a certain object or class. It’s employed in applications that require a greater level of input classification accuracy.

The following three types of image segmentation are available:

  • Semantic segmentation displays the boundaries between items that are comparable. When extreme precision is required regarding the existence, position, size, or form of objects inside a picture, this approach is applied.
  • The existence, position, quantity, and size or form of objects inside a picture are all identified via instance segmentation. As a result, instance segmentation aids in labeling the existence of each and every object inside an image.
  • Instance and semantic segmentation are combined in panoptic segmentation. As a result, panoptic segmentation gives data labeled for the image’s backdrop and object.

Image classification

It’s a sort of ML model in which the complete image is identified by a single label. The goal of the annotation process for classification models is to detect the existence of comparable items in the dataset’s pictures.

It’s being used to create an AI model to recognize an item in an unlabeled picture that resembles classes in annotated photos used to train the model.

Object detection

Goes a step farther than image categorization to determine the existence, position, and quantity of objects in a picture. The image annotation method for this sort of model involved drawing borders around every identified data in each picture, allowing us to determine the precise position and collection of instances contained in an image. As a result, the primary distinction is that classes are discovered inside a photo instead of the entire image being classed as a single class.

Conclusion

This same task of labeling a picture with data labels is known as an image annotation. The annotation process is normally done by hand with the assistance of a computer. These tools assist in the process of training computer vision models.