Image Annotation in 2024: Definition, Importance & Techniques
Image annotation is one of the most important stages in the development of computer vision and image recognition applications, which involves recognizing, obtaining, describing, and interpreting results from digital images or videos. Computer vision is widely used in AI applications such as autonomous vehicles, medical imaging, or security. Therefore, image annotation plays a crucial role in AI/ML development in many sectors.
You can also work with an AI data partner. Check out our guide to finding the right image data collection service that offers image annotation as a complementary service.
What is image annotation?
Supervised ML models require data labeling to work effectively. Image annotation is a subset of data annotation where the labeling process focuses only on visual digital data such as images and videos.
Image annotation often requires manual work. An engineer determines the labels or “tags” and passes the image-specific information to the computer vision model being trained. You can think of this process like the questions a child asks her parents to explore the environment in which she lives. The parents categorize the data into universal phrases such as bananas, oranges, cats, etc., as shown in the below image.
Why is image annotation important now?
Computer vision has already changed our lives with applications in healthcare, automotive, or marketing. According to Forbes, the computer vision market value will be around $50 billion in 2022 and PWC predicts that driverless cars could account for 40% of miles driven by 2030.
What are the techniques for image annotation?
There are five main techniques of image annotation, namely:
- Bounding box
A frame is drawn around the object to be identified. Bounding boxes can be used for both two- and three-dimensional images.
Landmarking is an effective technique for identifying facial features, gestures, facial expressions and emotions. It is also used to mark body position and orientation. As shown in the figure below, data labelers mark specific locations on the face, such as eyes, eyebrows, lips, forehead, and so on with specific numbers by using this information ML model learns the parts of the human face.
These are pixel-level annotations that hide some areas of an image and make other areas of interest more visible. You can think of this technique as an image filter that makes it easier to focus on certain areas of the image.
This technique is used to mark the pick point of the target object and frame its edges: The polygon technique is a useful tool for labeling objects with irregular shapes.
The polyline technique helps create ML models for computer vision that guide autonomous vehicles. It ensures ML models recognize objects on the road, directions, turns, and oncoming traffic to perceive the environment for safe driving.
How to annotate images and videos?
Your company needs an image annotation tool to label the visual data. There are vendors that offer such tools for a fee. There are also open source image labeling tools that you can use freely. Moreover, they are modifiable, which means you can change them according to your business needs.
Developing your own tool for image annotation could be an alternative to outsourcing software. However, like all in-house activities, this is a more time-consuming and capital-intensive approach. However, if you have sufficient resources and feel that the templates available on the market do not meet your requirements, developing your own tool is possible.
In-housing vs outsourcing vs crowdsourcing?
Image annotation techniques require some manual work. Deciding who should perform this manual task is an important strategic decision for organizations. It is because the main methods, namely in-house, outsourcing and crowdsourcing, offer different levels of cost, output quality, data security, etc.
It is important to note that there is no prescribed strategy for choosing between these methods. The optimal strategy will vary depending on the conditions and needs of your organization. Nevertheless, the following table might be helpful for you to select the optimal strategy. For more information, you can click here.
|Quality of labeling
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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