What is image annotation? Introduction to image annotation for ML
The performance of your machine learning model depends on the quality and accuracy of its training data.
Machine learning, an application of artificial intelligence, gave us speech recognition, traffic prediction, online fraud detection, to name a few, impacting our everyday lives.
Computer vision, an application of machine learning, gives computers the power of vision. Thanks to computer vision, computers can “see” and interpret the world around them, almost like humans do. Image annotation makes this possible and unlocks the power of technology.
This blog post covers all you need to know about image annotation to make informed decisions for your business. Here are the questions that this blog post will be covering:
- What is image annotation?
- What do you need to annotate images?
- What are the different types of image annotations?
- What are some image annotation techniques?
- How are companies doing image annotation?
- What are some image annotation use cases?
What is image annotation
Image annotation is the practice of labeling images to train machine learning models. It works by providing images and labels to human annotators and asking them to match the labels with the correct elements in the images. The resulting data is then fed to a machine learning algorithm, which can be used to train models.
For example, you can ask your annotators to annotate vehicles in a given set of images. The resulting data can help you train a model that can recognize and detect vehicles. Image annotation is commonly used in training models for autonomous driving. Thanks to that, self-driving cars can detect including but not limited to pedestrians, traffic lights, potholes, and other vehicles to navigate safely.
What do you need to annotate images
Different image annotation projects have different requirements. However, diverse images, trained annotators, and a suitable annotation platform are the building blocks of every successful annotation project.
Diverse images
You need hundreds if not thousands of images to train a machine learning algorithm that makes accurate predictions. The more images you have and the more diverse they are, the better.
Suppose you want to train a security camera to detect dogs. In this case, you will need images of different dog breeds of different sizes and in different lighting conditions to create a reliable model. Make sure your images cover almost all possible conditions to guarantee accurate predictions.
Trained annotators
A team of trained and professionally managed annotators is necessary to drive an image annotation project to success. Establishing an effective QA process, and keeping communication open between the annotation service and key stakeholders are crucial for an effective project execution. Providing the workforce with a clear annotation guideline is one of the best data labeling practices since it helps them avoid mistakes from the beginning. Also, make sure you provide regular feedback to your workforce for a more effective QA process.
Suitable annotation platform
Behind every successful image annotation project is a comprehensive and user-friendly annotation tool. When looking for an image annotation platform, make sure it includes the tools you need for your current and future projects. An integrated management system and quality management process are also necessary to track project progress and manage project quality.
Keep in mind that you may encounter technical issues, so make sure the image annotation platform you choose provides technical support through documentation and a support team.
What are the different types of image annotation
Image classification
Image classification is a task that aims to get an understanding of an image as a whole by assigning it a label.
Object detection
Unlike image classification, where a label is assigned to an entire image, object detection is the practice of assigning labels to different objects in an image. As the name suggests, object detection detects the target objects within an image, assigns them a label, and identifies their location.
Segmentation
Segmentation takes image classification and object detection a step further. This method consists of sectioning an image into multiple segments and assigning a label to each segment. Since each segment is made of pixels, each pixel is assigned a label, making annotation even more accurate.
There are three types of segmentation:
- Semantic segmentation
Semantic segmentation consists of dividing an image into clusters and assigning a label to every cluster. Let’s say we have an image of three sheep. In the case of semantic segmentation, each sheep will be counted as one cluster. The background, which consists of the sky, will be taken into consideration too.
- Semantic segmentation
Instance segmentation identifies the existence, location, shape, and count of objects. You can use instance segmentation to identify how many people there are in an image, for example. Let’s go back to the sheep example. In the case of instance segmentation, each sheep will be counted as separate instances, even though they are assigned the same label.
- Panoptic segmentation
Panoptic segmentation is where instance segmentation and semantic segmentation meet. It classifies all the pixels in the image (semantic segmentation) and identifies to which instances these pixels belong to (instance segmentation). In our example, all the pixels in the image will be assigned labels, but each sheep will be counted separately.
What are some image annotation techniques
There are many image annotation techniques, but it doesn’t mean you should work with all of them. Getting a firm grasp of the most common image annotation techniques is important to understand what your project needs and what annotation tool you need to choose.
Bounding boxes
Bounding boxes are used to draw rectangles around symmetrical objects such as furniture, trucks, and parcels. This helps algorithms detect and locate objects, which is crucial for autonomous vehicles, for example. Annotating pedestrians, traffic signs, and vehicles help self-driving cars navigate safely on the roads. Bounding boxes can be two-dimensional or three-dimensional (cuboids).
Polygons
Polygons are used to annotate the edges of objects that have an asymmetrical shape such as rooftops, vegetation, and landmarks.
Polylines
Polylines are used to annotate line segments such as wires, lanes, and sidewalks.
Key-points
Key-points are used to annotate small shapes and details by adding dots around the target object such as facial features, body parts, and poses.
How are companies doing image annotation?
Image annotation is a significant investment that costs time and money, so consider your project size, budget, and delivery time before choosing how to carry out your image annotation project.
In-house
Manage your image annotation project with the resources available at hand. Go with this option to save money, guarantee data privacy and security, and have direct oversight over your project. However, in-house image annotation can be time-consuming if your team members need training. For faster and more successful image annotation project delivery, consider outsourcing your project.
Outsourcing
Leave it to the experts when it comes to delivering quality results on time. When outsourcing to image annotation service providers, make sure that the workforce is trained, vetted, and professionally managed to save yourself a headache. Better yet, run a pilot project to evaluate the performance and quality of the image annotation service provider.
Crowdsourcing
If you’re lacking resources, you can always crowdsource your image annotation project.
What are some image annotation use cases
Image annotation is being used to build technologies that you’re using every day.
Face recognition
Image annotation is used in developing facial recognition technology. It involves annotating images of human faces using key-points to recognize facial features and distinguish between different faces.
Security and surveillance
One of the common image annotation applications is surveillance to detect items such as suspicious bags and questionable behavior.
Agriculture technology
Agriculture technology relies on image annotation for various tasks such as detecting plant diseases. This is done by annotating images of both healthy and diseased crops.
Medical imaging
Image annotation is commonly used in the medical field. For example, by annotating images of benign and malignant tumors using pixel-accurate annotation techniques, doctors can make faster and more accurate diagnoses.
Robotics
Robots depend on image annotation to perform tasks such as sorting parcels, planting seeds, and mowing the lawn, to name a few.
Wildfire detection
Believe it or not, but image annotation is saving nature. Drones are used in wildfire management to locate and detect fires to prevent them from causing further damage.
Conclusion
Artificial intelligence, with the help of machine learning, is one of the driving forces of the fourth industrial revolution, impacting all industries from healthcare to agriculture and security. Image annotation is one of the ways to create better and more reliable machine learning models, hence, more advanced technologies.
Remember that your machine learning model is as good as your training data. So if you have a large amount of accurately labeled images, you can build a model that delivers excellent results.
Now that you know what image annotation is, the different image annotation types, techniques, and use cases, you can take your business to the next level. Are you ready to get started?
Originally published at https://blog.superannotate.com.
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