Image Annotation For Computer Vision

For any artificial intelligence project to be a success, the images used to train, validate, and test your computer vision algorithm plays a key role. To properly train an AI model to recognize objects and make predictions just as humans do, we thoughtfully and accurately label images in every data set.

The more diverse your image data is, the trickier it gets to have them annotated in line with all your specifications. This can end up being a setback for both the project and its eventual market launch. For these reasons, the steps you take in crafting your image annotation methodologies, tools, and workforce are all the more important.

Image Annotation for Machine Learning

What is image annotation?

In both deep learning and machine learning, image annotation is basically labeling or categorizing images through an annotation tool or text tools to convey data attributes that the AI model is training to recognize. When annotating an image, you’re basically adding metadata to a data set.

Image annotation is a branch of data labeling also known to as tagging, transcribing, or processing. Videos can also be annotated, either frame by frame or as a stream.

Types of images used for machine learning

Machine learning involves annotation of both images and multi-frame images e.g. videos. As earlier indicated, videos can either be annotated frame by frame or as a stream.

There are usually two data types used in image annotation. They are;

  1. 2-D images and video
  2. 3-D images and video

How to Annotate Images

Images are annotated using image annotation tools. These tools are either are available in the open market, freeware, or open-source. Depending on the volume of data on hand, the need for an experienced workforce to annotate data comes to play. Data annotation tools come with a set of capabilities that a workforce can utilize to annotate images, multi-frame images, or video.

Methods of image annotation

There exist four methods of image annotation for training your computer vision or AI model.

  1. Image Classification
  2. Object detection
  3. Segmentation
  4. Boundary Recognition

Image Classification

Image classification is a branch of image annotation that works by identifying the presence of similar objects in images across a dataset. Assembling images for image classification is also known as tagging.

Object Recognition/Detection

Object recognition works by identifying the presence, location, and number of either one or more objects in an image and accurately labeling them.

Depending on compatibility, we use different techniques to label objects within an image. These techniques include bounding boxes and polygons.


Segmentation annotation is the most complex application of image annotation. We use Segmentation annotation in a number of ways to examine visible content in images and decide if objects within the same image match or differ. There are three types of segmentation:

  1. Semantic Segmentation
  2. Instance Segmentation
  3. Panoptic Segmentation

Boundary Recognition

We can train machines to identify lines/boundaries of objects within an image. Boundaries can consist of edges from a particular object, topography areas shown in the image, or any man-made boundaries that appear on the image.

When accurately annotated, we use images to teach an AI model on how to see akin designs in unlabeled images.

We use boundary recognition to teaching AI models on how to identify international boundaries, pavements, or even traffic lines. For the eventual safe use of autonomous vehicles, boundary annotation will play a very key role to make it all possible.

How to do Image Annotation

In order to make annotations in your image data, you need a data annotation tool. And with the advent of AI, data annotation tools use cases have propped up all over the globe.

Depending on your project needs and the resources at your disposal, you can tailor-make your own annotation tool. If you take this path, you will need resources and experts to continuously maintain, update, and improve the tool over time.

Image Annotation Methods

Depending on your annotation tool’s feature sets, image annotation comprises the following techniques:

  1. Bounding Box
  2. Landmarking
  3. Polygon
  4. Tracking
  5. Transcription

Bounding Box

This technique world by drawing a box around the object in focus. This method works well for relatively asymmetrical objects e.g. road signs, pedestrians, cars, etc. We also use bounding boxes when we have less interest in an object’s shape and when there are no strict rules on occlusion.


Landmarking works by plotting characteristics in data. We mainly use it in facial recognition technology to detect emotions, expressions, and facial features.


The polygonal annotation works by marking each of the highest points (vertices) on the target object and annotating its edges. For this reason, we use polygonal annotation when the object is of a more irregular shape e.g. houses, land, vegetation, etc.


We apply tracking to tag and plot an object’s motion through several frames in a video.

A number of annotation tools have interpolation attributes that permit annotators to tag each frame at a time.


We apply transcription when annotating text in an image or video. Annotators use this when there is different information (i.e. image and text) in the data.

How Organizations are Doing Annotations

Companies employ a blend of software, processes, and people to collect, clean, and annotate images. Generally, organizations have four options when selecting an image annotation workforce. The quality of work is dependent on how well the team is managed and how their KPIs are set.


This involves having people on your payroll, either part-time or full time. This allows you to mold in house expertise and be adaptable to change. However, scaling up when using an internal team may prove to be a challenge. This is because you take full responsibility and expenses in hiring, managing, and training workers.


Contractors are freelance workers who train to do the work. With contractors, there is some flexibility in the event that you want to scale up. However, just like employees, you will take responsibility for managing the team.


Crowdsourcing is an anonymous, make-do source of labor. It works by using third party platforms to reach large numbers of workers. Subsequently, the users on the platform volunteer to do the work described to them. With crowdsourcing, there is no guarantee for landing annotation experience and you are constantly in the dark with regard to who is working on your data. As a result, the quality will be low since you cannot vet crowdsourced workers in the same way as in-house employees, contractors or managed teams are.

Managed Teams

Managed teams are basically the outsourcing route. A managed team applies professionalism in both training and management. It works by you sharing your project specifications and annotation process. In return, the managed teams aid in scaling up when the need arises. As the team continues working, their domain knowledge with your use case is likely to improve with time.

Advantages of Outsourcing to Managed Teams

  1. Training and Context

To get high-quality data for machine learning, basic domain knowledge, and understanding of image annotation is a must. A managed team guarantees high quality labeled data. This is because you can teach them context, relevance, and setting of your data. Consequently, this guarantees that their knowledge only increases over time. Unlike crowdsourcing, managed teams have staying power and are able to retain domain knowledge.

  1. Agility

Machine learning being an iterative process, you may need to alter project rules and workflow as you validate and test your AI model. With a managed team, your ensured flexibility to integrate changes in data volume, task duration, and task complexity.

  1. Communication

With a managed image annotation team, you can create a closed technological feedback loop. This ensures seamless communication and cooperation between your internal team and annotators. In this way, workers are able to share insights on what they noticed when working on your data. With their insights, you can opt to adjust your approach.



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