Image Annotation in Machine Learning: Process and Prerequisites

The new-age technologies AI and ML have a profound impact on our everyday life in ways like online fraud detection, traffic prediction, and speech recognition to name a few. At the core of these marvels is a Machine Learning application called Computer Vision that enables computers to ‘see’ and interpret the world around them, much like the way humans do.

The performance of a Computer Vision-based model highly depends on the accuracy and quality of its training data, which is essentially composed of annotated images and videos. Thus, data annotation is the process that fuels these Machine Learning algorithms.

Process at Glance

Image annotation is the practice of adding labels to the images to be fed into the AI/ML models. The process often involves human annotators leveraging an annotation tool to tag relevant information or add context, for example, by assigning relevant classes to diverse entities in an image.

In other words, labels are added to outline the target characteristics of your input data on a human level. The resultant data—also referred to as structured data is fed to a Machine Learning algorithm that is often understood as a training model. Depending on the quality of the data, you can achieve the desired level of accuracy in Computer Vision tasks.

For instance, you can ask annotators to label all the vehicles in a given set of images. The resulting data, which is in a properly structured format can help train a CV model that can accurately detect and recognize vehicles. With further training, it can discriminate vehicles from traffic lights, lanes, streets, potential obstacles, or pedestrians on the road to navigate safely. Here, an autonomous vehicle or driverless car is one example of how image annotation fuels Computer Vision. There are numerous other real-world use cases of image annotation for Machine Learning.

Image Annotation Prerequisites

Different image labeling projects can have different requirements. However, a vastitude of images, skilled annotators, and a suitable infrastructure are the building blocks of every successful annotation project.

• Diverse Images

Businesses need hundreds, if not thousands of images to train a Machine Learning algorithm that makes accurate and reliable predictions. The more distinct images you have, the better for you. Ensuring that your images cover almost all possible conditions guarantees reliability and precision in prediction results.

You want to train a security camera, let’s say, to detect suspicious behavior or criminal activity. In such a situation, you’ll need images of the given street in different lighting conditions which are also clicked from different angles to create a reliable model.

• Skilled Annotators

A team of skilled and competent annotators is necessary to make any image annotation project successful. Establishing an effective QA (quality assurance) process as well as keeping communication open between the service provider and key stakeholders/management is crucial for effective project execution. One of the best data labeling practices is to provide a clear annotation guideline to professionals as it helps them avoid mistakes before they are set for training.

• Adequate Infrastructure

Behind every successful image annotation for machine learning is a functional and user-friendly labeling tool. When looking for such a platform, ensure it has the features needed to cover your ongoing use cases. Other than this, an integrated management system, as well as quality a management process, are also necessary to track progress and manage quality.

Enterprises might have to encounter technical issues, so it is advised to opt for image recognition services that provide 24*7 technical support through documentation and a dedicated team.

Way Forward

Image labeling is a significant investment in your AI/ML project implementation efforts that costs resources like time and money. So, carefully consider your project size, delivery time, and budget before choosing how to carry out your image annotation project. The three different ways you can go ahead with are:

• In-house Set Up

One way to manage your image annotation project is by leveraging the resources available at hand. Businesses can either have an in-house team of annotators to do the job if it's a small-scale experimental project.

With a team of annotators, you should ensure that there's also a QA process involved as you are responsible for errors in data. To avoid an increasing number of errors and poor model performance, an internal team of labelers will need proper training and instructions along with expert guidance. So, if you're leaning towards a faster way to label images while maintaining a high quality of outcomes, consider image annotation outsourcing for your project.

• Outsourcing

One of the tangible advantages of outsourcing is you can leave it to the professionals to deliver quality results on time. When engaging in image annotation services, you have to be extra picky in the workforce to ascertain they are well-trained, assessed, and professionally managed to save you more than a headache.

Better of all is to run a pilot project to evaluate the external vendor’s performance and see if the results are in line with your project objectives. If your data is too niche-specific, for example, DICOM images that need annotators expert in the medical field, the teams can offer you subject matter expertise.

• Crowdsourcing

You can always crowdsource your image annotation project if you’re lacking resources. Using crowdsourced solutions for Computer Vision projects is a commonly used method, which is also time-saving and affordable at scale. However, the downside of this is insufficient or poorly organized quality control. So, whatever the case, ensure to keep the communication open and provide consistent feedback once you decide to move on with this option.

Final Thoughts

AI and ML are the driving forces of the modern tech environment, impacting all industries—ranging from security, sports, and healthcare to agriculture and much more. And, image annotation is one of the ways to create improved and more reliable Machine Learning algorithms and hence, more advanced technologies. So, it wouldn’t be wise to neglect the role of image annotation.

Know that a Machine Learning model is as good as the training data it is fed with. Therefore, if you have a large amount of accurately annotated images, you can build a fail-proof and reliable model that delivers excellent results.