What is Image Annotation and Why is it Important in Machine Learning?

What Is Image Annotation? 

Image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords in a digital image. Data labelers use tags, or metadata, to identify characteristics of the data fed into an AI or ML model to learn to recognize things the way a human would. Tagged images are then used to train the algorithm to identify those characteristics when presented fresh, unlabeled data. 

Image annotations are important drivers of computer vision algorithms because they form the training data that is input to supervised learning. If the annotations are of high quality, the model will “see” the world and create accurate insights for the application. If they are low quality, ML models will not provide a clear picture of relevant real-world objects and will not perform well. Annotated data is particularly important when the model is trying to solve a new field or domain.  

Types of Image Annotation

There are several key forms of algorithm-based image annotation methods that are used by ML engineers. 

Bounding Box Annotation

Entails making a rectangular drawing of lines from one corner of an object to another in an image, based on its shape. 

Polygon Annotation

Boundaries of an item in a frame are annotated with high precision, allowing the object to be identified with the right size and form. Polygon annotation is common for recognizing things like street signs, logo images, and facial recognition. 

Cuboid Annotation

This 3D type of annotation involves high-quality labeling and marking to highlight 3D drawing forms. It is used to determine the depth or distance of items from things like buildings or cars and helps identify space and volume, so it’s common in construction and medical imaging. 

Text Annotation

Language can be very difficult to interpret, so text annotation helps create labels in a text document to identify phrases or sentence structures. It helps prepare datasets for training so that the model can understand language, purpose, and even emotion behind the words. 

Semantic Segmentation

Also known as picture segmentation, this type groups sections of an image that are part of the same object class. Pixels in an image are categorized to create a pixel-level prediction. 

Use Cases for Image Annotation

With the help of digital photos, videos and ML models, computers can learn to understand visual environments as humans do. High-quality annotations help drive the accuracy of computer vision models that are used in an increasingly wide range of applications. 

Autonomous Vehicles

ML algorithms for autonomous cars must of course be able to recognize things like road signs, traffic lights, bike lanes, and other potential road risks like bad weather. Picture annotation is common in various areas, such as advanced driver-assistance systems (ADAS), navigation and steering response, road object (and dimension) detection, and movement observations (such as with pedestrians). 

Surveillance and Security

Security cameras are everywhere these days, and companies are throwing large sums into surveillance equipment to avoid theft, vandalism, and accidents. Image annotation is used in crowd detection, night and thermal vision, traffic motion and monitoring, pedestrian tracking, and face identification. ML engineers can train datasets for video and surveillance equipment using annotated photos to provide a more secure environment. 

Agriculture

Even farmers are getting in on the game. Image annotation helps create content-driven data labeling to reduce human injury and protect crops. It also simplifies common agricultural tasks such as livestock management and the detection of unwanted or damaged crops. 

Key Challenges for Image Annotation in ML

While the benefits of deploying image annotation are plentiful, there are also a number of key challenges ML engineers and data science teams face. 

• Selecting the Right Annotation Tools

ML algorithms must be taught to recognize entities within digital visual images the way humans do. Organizations must understand what aspects of data types they want to use for data labeling, and they will need the right combination of digital annotation tools and a workforce that knows how to use them optimally. 

• Choosing Between Automated and Human Annotation

Using human resources to conduct image annotation, rather than computerized tools, can take more time and can add costs of finding the right engineers with the proper skillsets. Digital annotation performed with computerized tools provides a better level of accuracy and consistency. 

• Ensuring Quality Data Outputs

ML business models rely heavily on high-quality data outputs, but those ML models can only build precise projections if the data quality is indeed trusted. Subjective data can be hard for digital labelers to interpret depending on where they are geographically located, for example. 

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