Let’s forget some of the most complex Artificial Intelligent (AI) systems out there, such as those in self-driving cars, robotic arms, and more, for a while and just focus on the systems on our smartphones. Consider a comparatively simpler application like Google Lens, which uses computer vision for image annotation and recognition, to show you information on the photos you click using your device’s camera and more. With translation features, this application represents the commercial application of AI in its finest form.
However, what appears simple is tedious to develop and deploy like any other complex AI system. Before your device could recognize the image that you capture and the Machine Learning (ML) modules could process it, a data annotator or a team of them would have spent thousands of hours annotating data to make them understandable by machines.
In simple words, image annotation is very similar to the process of teaching a kid the names of fruits from a book. When you sit down to teach them, you point your finger at the image of an apple and teach them what an apple is and how they look and feel like. In machine learning, this happens virtually. Instead of fingers pointing out elements in an image, image annotators use diverse techniques to teach a system how to identify image elements, classify them, and process them for optimum results.
To give you a better idea of the different image annotation techniques, we have curated a list of image annotation techniques that you will find interesting and useful. So, if you’re a tech enthusiast, an entrepreneur looking to develop an AI-driven product, or an aspiring ML expert, you will find these immensely resourceful.
Let’s get started.
5 Most Popular Image Annotation Techniques
In this technique, image annotators manually draw boxes on different elements in an image they are tasked to work on. They draw precise boxes that cover all possible edges of the element for machines to identify what that particular object is accurately.
For instance, if annotators had to label an image of a landscape, they would draw boxes over mountains, rivers or water bodies, meadows or the ground, sky, clouds, sun, moon, or whatever element the image contains. To do this, businesses either use commercial tools or customized versions to suit their work needs.
When developing software for autonomous cars, image annotators would draw boxes over pedestrians, cars, objects on the road, and more to classify different elements.
This is very similar to the bounding box technique. The only difference here is that annotators have to draw 3D cuboids over objects to specify three essential attributes: length, depth, and breadth.
In some cases, certain portions of an object get hidden behind other elements. At times like these, annotators approximately draw a cuboid over the image to bring out the depth.
An interesting use case of drawing 3D cuboids, over mailboxes or trash cans on the roads for cars to precisely park by the lanes.
Polygons are super-precise and drastically reduce the noise created by the other two techniques. For elements and images that are not bound by a particular shape or size, image annotators encapsulate them by placing dots around the corners of an element and connecting them with lines. The result is an accurate encapsulation of the element.
This is more relevant and useful in aerial shots of landscapes, where there are too many elements close to each other, and bounding boxes would cause an overlap when drawn. Water bodies, buildings, landmarks, and other irregular shapes can be easily contained within polygons.
As the name suggests, this image labeling technique involves annotators drawing straight lines to classify that element as a particular object. Line segmentation helps establish boundaries, define routes or pathways, and more.
One of the major use cases of line drawing lies in differentiating lanes in an avenue for cars to identify and precisely drive themselves. Through line segmentation, autonomous vehicles can know which lane is ideal for what speeds, incoming lanes, areas to change lanes, and similar actions. This technique is also used in warehouses to train robots to pick up or place boxes from aisles and conveyor belts.
If you notice, all the techniques previously discussed involve only the outlines of objects in an image and not their complete shapes and forms. Semantic segmentation is where this precision outlining happens. In this technique, every individual pixel in an image is tagged manually.
To achieve precision, annotators use the polygon technique to club pixels they want to tag together and assign them a unique color code for differentiation.
Semantic segmentation is used in complex computer vision applications like tagging brain lesions. It is also used in computer vision modules in autonomous cars to add more details to road elements that would be hard to achieve through other techniques.
Now you understand the insane amounts of effort that go into computer vision, right? For every seamless action we execute and experience now, there have been swarms of data scientists and annotators putting countless hours of effort into optimizing their image recognition modules.
So, if you’re developing an AI-powered model, this phase of development is inevitable. However, you could skip this by associating with expert data annotators like us to do all the manual work.