Looking for information on the different image annotation types? In the world of AI and machine learning, data is king. Without data, there can be no data science. For AI developers and researchers to achieve the ambitious goals of their projects, they need access to enormous amounts of high-quality data. In regards to image data, one major field of machine learning that requires large amounts of annotated images is computer vision.

What is Computer Vision?

Computer vision is one of the biggest fields of machine learning and AI development. Put simply, computer vision is the area of AI research that seeks to make a computer see and visually interpret the world. From autonomous vehicles and drones to medical diagnosis technology and facial recognition software, the applications of computer vision are vast and revolutionary.

Since computer vision deals with developing machines to mimic or surpass the capabilities of human sight, training such models requires a plethora of annotated images.

What is Image Annotation?

Image annotation is simply the process of attaching labels to an image. This can range from one label for the entire image or numerous labels for every group of pixels within the image. A simple example of this is providing human annotators with images of animals and having them label each image with the correct animal name. The method of labeling, of course, relies on the image annotation types used for the project. Those annotated images, sometimes referred to as ground truth data, would then be fed to a computer vision algorithm. Through training, the model would then be able to distinguish animals from unannotated images.

While the above example is quite simple, branching further into more intricate areas of computer vision like autonomous vehicles requires more intricate image annotation.

What are the Most Common Image Annotation Types?

Wondering what image annotation types best suit your project? Below are five common types of image annotations and some of their applications.

1. Bounding Boxes

For bounding box annotation, human annotators are given an image and are tasked with drawing a box around certain objects within the image. The box should be as close to every edge of the object as possible. The work is usually done on custom platforms that differ from company to company. If your project has unique requirements, some companies can tweak their existing platforms to match your needs.

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