The Ultimate Guide to Image Annotation

The Ultimate Guide to Image Annotation

Are you curious about image annotation?

It serves as a foundation for many Artificial Intelligence (AI) products you use. One of these is computer vision, which seeks to give machines intelligent “eyes” that help them recognize and interpret objects and the world around them.

This tool is used for various applications, from teaching self-driving cars to inspecting buildings after natural calamities. Indeed, this area of AI development and machine learning is revolutionizing a wide variety of fields.

Machines must develop enough intelligence to mimic or surpass the capabilities of human sight. This involves feeding computers with a lot of data. And that’s where image annotation comes in.

This guide is a simple introduction to the complexities of image annotation, so read on!

What Is Image Annotation?

Image annotation is the human-powered process of adding labels to a picture. This can either be a single label for the whole image or various labels for different pixel groups in the image.

The AI engineer determines the labels beforehand. Then, they give the computer vision model information about what’s in the image.

Data labelers make use of tags or metadata. They can identify the data characteristics the AI model needs to learn to recognize.

The process is like teaching a child. Children aren’t born with the knowledge of what a dog is. But, after seeing many dogs, they learn to distinguish them from other animals.

In the same way, computers need examples to learn to recognize objects.

How Does Image Annotation Work?

The image annotation process needs three things. First are the images, then human annotators, and finally, a platform like to process the annotation.

A project will begin when annotators are found and trained to do the task. Most annotators don’t have degrees in machine learning. However, annotators receive training on the specifications and guidelines for each project.

Annotators might, for example, review images of animals. They’ll label every image with the correct name of the animal they’re shown. People in the industry sometimes call the annotated images “ground truth data.”

After annotation, these images are loaded into a computer vision algorithm. Through this process, the model learns how to recognize the animals.

They’ll then be able to recognize objects from unannotated images.

Types of Image Annotation Services

Determining the right service for a project depends on the project’s complexity. The higher the quality of image data used, the more accurate the resulting AI predictions will be.

Image Classification

Classification attaches only one tag to an entire image. As a result, it’s the easiest and fastest image annotation service.

For example, annotators might be tasked with looking through images of grocery store shelves. They’ll classify which shelves contain soda and which don’t. In other instances, annotators might be asked to identify the time of day or filter out specific images.

Classification is ideal for recording abstract information. The process provides a single, high-level label quickly. However, it’s also relatively vague because you can’t identify where an object is in an image.

Bounding Boxes

With bounding boxes, annotators have to draw a box around the objects they want to identify. The container must be as close as possible to every edge of the object.

In some projects, the target objects will be the same–for example, drawing boxes around every car in the image.

At other times, there could be a category of target objects. For example, annotators must draw boxes around every car, bicycle, and pedestrian in an image.

After drawing the box, annotators assign a label–chosen from a pre-decided list–to the object.

3D bounding boxes or cuboids are a variation of this technique. Annotators also draw boxes around objects. However, the boxes are three-dimensional and also show the depth of the target object.

Lines and Splines

Annotators use lines and splines to, as the name suggests, label straight or curved lines in images. All they need to do is simply draw lines along boundaries in an image.

Lines and splines are often used for training programs to recognize lanes, sidewalks, power lines, and other physical boundaries. This type of annotation is also used for planning drone trajectories and training warehouse robots to accurately place boxes or items in rows or on a conveyor belt.

Most commonly, however, AI specialists use lines and splines annotation to train self-driving vehicles.


Target objects can sometimes be irregular. They might have an asymmetrical shape, orientation, or size within an image. This means they can’t fit well in bounding boxes or cuboids.

Developers sometimes want more precise annotations for target objects, and that’s where polygon annotation comes into play. Annotators can use polygons to draw points on every vertex of a target object. They can annotate an object’s exact edges, regardless of the shape.

Just as with bounding boxes, the annotated object will then be labeled with a description.

Semantic Segmentation

Unlike the other types of annotation, which outline individual objects in images, semantic segmentation associates individual pixels in an image with a tag. It’s a more specific type of annotation.

In semantic segmentation projects, annotators usually get a list of predetermined tags. They can choose from this list and assign a tag to segments of the image.

For example, when annotating images for self-driving vehicles, they would divide the image into cars, roads, pedestrians, and other segments using specific tags. Each segment has a unique color code.

The resulting single image will contain annotations for multiple objects.

Image Annotation: Essential for Machine Learning

Image annotation is a foundational method for building computer vision. AI specialists use prepared images to teach algorithms.

Different types of image annotation services vary in the objects they target and in their accuracy. Each method, however, is reliant on human annotators to assign labels to objects within an image.

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