A Comprehensive Guide for Image Annotation in Machine Learning

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image annotation in machine learning
Image Annotation

Have you ever wondered how self-driving cars have become a reality? Take other examples like speech recognition, online fraud detection, traffic prediction, biometric identification, and so on are all making our lives easier. But what is the driving force behind them?

Machine Learning, an application of Artificial Intelligence, has given us such tech marvels that impact our daily lives. Computer Vision, an application of Machine Learning gives vision to computers. So, in the literal sense, Computer Vision is the ability of computers to ‘see’ the world around them, much as humans do. Image annotation is what unlocks the power of technology and makes this possible.

This piece covers everything businesses must know about image annotation—its meaning, types, challenges in the process, and which approach is the best. Take a look…

Know the Concept

Image annotation is the foundation behind numerous AI/ML applications that we interact with regularly. In practice, it is the process of adding tags, labels, and descriptions to the different objects of interest present in an image. Using these labeled datasets, the Machine Learning algorithms detect and identify different things in their surroundings, when presented with fresh, unlabeled data.

Consider the case in point: think about when you were a child. You learned what a dog was at some point in time. you understood the different breeds of dogs after seeing many dogs, and how a dog was different from a pig or a cat, over the course of time.

Similarly, for computers to learn how to categorize things, it needs examples. Accurately labeled image datasets provide such examples in a way that is easy for these machines to comprehend. With the volumes of image data readily available for organizations implementing AI/ML, the number of projects depending on image annotation has grown exponentially.

Listed here are different types of image annotation:

Image Classification

As the name suggests, image classification enables machines to classify different objects in images and across an entire dataset. Different tags and labels are added to the images so that the Machine Learning algorithm can classify different objects in its surroundings. For example, exterior images of a building can be tagged with labels such as “balcony”, “fence” or “garden” and interior images of the building as “stairs” or “elevator”.

Object Detection & Identification

Object detection enables machines to identify a particular object of interest, detect the presence of multiple objects, including the number of locations and instances, and label them accurately.

The same process can be applied to different image datasets to train the AI/ML model to autonomously identify these objects in new images. Object identification-compatible techniques such as bounding boxes, polygons, or point cloud labeling help in labeling different objects in a single photo. For example, you can annotate cycles, bikes, buses, cars, and pedestrians separately in a single image.

Image Segmentation

Image segmentation helps Machine Learning models to locate boundaries and different objects in a photo. This technique offers higher accuracy for tasks that are related to object classification. In practice, an image is divided into several segments where every pixel is assigned specific classes or class instances, depending on the model’s future use case.

Landmarking

This technique enables machines to identify expressions, facial features, gestures, and emotions. Labelers use landmarking techniques to mark the orientation and different positions of a human body.

For example, the landmarking technique can be used to mark specific different parts of the face such as the forehead, eyebrows, eyes, and lips, with specific numbers. As a result, the Machine Learning algorithms can use these marks to learn about the different parts of a human face.

Boundary Recognition

Boundary recognition allows the Machine Learning algorithms to identify the lines or boundaries of different objects in an image. These boundaries can either be regions of topography or edges of a specific object. An annotated image enables the AI/ML models to identify similar patterns in fresh images. This technique is especially helpful in training self-driving cars to operate safely.

Challenges in Image Annotation

It is important to know that any AI model is as smart as the data it is fed with. Adding accurate labels to every pixel of the image is a significant undertaking. Mentioned here is a list of some of the notable challenges that most organizations face in this process:

Balancing Costs with Accuracy Levels

Primarily, there are two image data annotation methods—automated annotation and human annotation. Human annotation generally costs more and takes longer as compared to automated annotation. Besides, finding skilled annotators is a task, but ensures more accurate results. On the contrary, automated annotation is cost-efficient, yet determining the quality and accuracy levels of the results becomes difficult.

Choosing the Right Infrastructure & Architecture

Though there are numerous image annotation tools and platforms with each offering different capabilities for different types of annotations, selecting the right one is a task. These tools and platforms have serious limitations and cannot be relied on for bulk operations. Besides, they cannot be customized according to unique business requirements.

Guaranteeing Steady Stream of Data

There are issues around personnel and processes. Training Artificial Intelligence and Machine Learning models require a steady stream of good quality data to make accurate predictions. Owing to different cultures, beliefs, and personal biases, the data annotators might interpret subjective data differently. So, if data is labeled inconsistently, the outcomes of Machine Learning models will also be skewed. Putting it simply, making Computer Vision operational needs the active participation of subject matter experts.

Security Considerations

Data security is also an important consideration. You must look for the answers to questions like is the right intel being accessed by the right analysts? Are the outputs of Machine Learning algorithms being classified appropriately? Does handling secret/top-secret imagery put added constraints on your organization’s processing capacity? Similar kinds of restrictions exist around the use of personally identifiable information (PII). Failing to abide by the data-related laws and compliances can lead to serious troubles.

Taking the Right Approach

Even though you get the right infrastructure, architecture, and resources that support such an implementation, you might have a head start in Computer Vision.

Many hurdles still have to be crossed internally. The Cloud can eat all the photos and video you want to feed it, but you’ll still need sufficient internal resources to ingest what Computer Vision has to offer. Apart from the scalable systems, you’ll need the ability to feed the newly generated intelligence into existing systems. So, what are the processes that you’ll need to ingest, store, and ultimately use the newly generated information?

These are the technical issues that businesses can easily overcome by collaborating with the right outsourcing partners. Apart from this, industry leaders have to go beyond the mechanical details.

For example images of a domestic situation have to be reviewed. Prior permissions are needed if it has to be videotaped, especially in jurisdictions. Then there has to be a proper person to train the AI/ML model who best understands the complications and potential risks involved in the case.

Overall, teaching a computer what a tree looks like isn’t hard. But there are different types of trees and they look poles apart in still photos and videos, in darkness and sunlight, on the mountains or the plains. If you want the Computer Vision to see what you see, that information must be conveyed accurately and effectively. Hence, there must be a human expert who has to be at the center of the situation.

Conclusion

Artificial Intelligence combined with Machine Learning is impacting all industries and verticals ranging from healthcare, banking and finance, insurance, and agriculture to security, and so on. Image annotation is one of the best ways to create improved and more reliable smart models.

A press release dated April 2022 states “the global Computer Vision market size is projected to reach USD 13380 million by 2028, from USD 8720 million in 2021, at a CAGR of 6.2% during 2022-2028.” This is just one application of Machine Learning powered by image annotation. You can calculate for yourself what potential this process holds.

Now that you know what image annotation is, the different types, challenges in the process, and the right approach, you can easily take your organization to the next level. So, are you ready?

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