Have you ever wondered how machines ‘see’ and ‘interpret’ things like humans do—through image annotation? It is the process of adding tags and descriptions to the input image datasets used to teach Machine Learning algorithms through supervised training. These labels help in identifying and understanding different objects within an image.
For instance, you learned what a dog was at some point in time when you were a child. After seeing so many dogs, you gradually learned about the different breeds of dogs and how a dog was different from a cat, a cow, or other animals. Similarly, machines need examples to detect, identify, and interpret different things in their surroundings—image annotation provides these examples in a computer-comprehensible language.
Image annotation project requirements vary according to AI/ML model’s future use cases. But overall, there are some basic requirements that are the building blocks of every successful data annotation project. These are:
If not thousands or upwards, you need at least hundreds of images to build an AI/ML model that makes accurate predictions. The more the number of independent images (meaning more miscellaneous and representative of the surrounding they are), the more reliable will be the outcomes. Let’s say you want to train a security camera to detect suspicious behavior and prevent criminal activity. Here, you’ll need images taken from different angles in different lighting conditions of the given street to create a reliable model.
A team of skilled annotators and data professionals is necessary to ensure the success of any image annotation project. You can also rely on image annotation services for this. But make sure to establish an effective QA process and keep communication open between the key stakeholders. One of the best practices in the data labeling process is to provide the workforce with a clear annotation guideline that is synced with your project goals and business objectives to avoid mistakes. Try to provide regular and as detailed feedback as possible after thoroughly considering its influence on possible edge cases.
To make any image annotation project successful, you need adequate infrastructure and access to the right tools. You have choices: either to go for manual or automated image annotation. Human annotators label your images using a combination of techniques and tools; whereas computer systems automatically assign metadata to the image. The second one is more like relying on supervised Machine Learning algorithms for the image annotation process. Whatever method you decide, ensure that you are equipped with the potential required.
- Data Quality and Relevance
Businesses should know that the quality of AI/ML model results is directly related to the quality of input datasets. Hence, you must be mindful of the quality of the data, its relevance, and its accuracy. The more relevant and precise input training sets are, the more reliable and unbiased the Machine Learning algorithm’s outcomes.
Types of Image Annotation
On a broader note, there are three types of image annotation as listed here:
- Image Classification: Here, the entire image is assigned a single label. The thumb rule is that ‘classification’ is applicable to images with only one object—where you only need to identify the general category of an image. For example, if you have a dataset of flower images, you might label each image as ‘rose,’ ‘lotus,’ ‘hibiscus,’ or ‘tulips.’
- Object Detection: Object detection is the practice of identifying and localizing different objects in an image. Unlike image classification, where an entire image is tagged with a label, object detection involves labeling different objects in an image. For example, if you have an image of a busy street, you’ll assign different labels to different objects such as ‘traffic signal,’ ‘vehicle,’ ‘pedestrians,’ etc.
- Segmentation: Segmentation is a step ahead of image classification and object detection. An image is sectioned into multiple segments where a label is added to each segment. In simple words, segmentation is the process of pixel-level classification and labeling. It is used for complex tasks that need more developed precision.
Image Annotation Techniques
Getting a firm grip on some of the most common image annotation techniques is important. You understand your project needs better and can identify what kind of annotation tool to use to address the requirements.
Rectangles or bounding boxes are drawn around symmetrical objects like traffic signs, furniture, trucks, parcels, etc. Input datasets with bounding boxes help computer algorithms to detect and locate objects easily. This is what the autonomous vehicle or self-driving car industry relies on, for example. Labeling pedestrians, lanes, potholes, and vehicles with bounding boxes helps self-driving cars to navigate safely on the roads and avoid accidents.
This type of annotation involves an extra dimension in a 3D plane to determine the depth or height of things such as buildings or cars. In addition, cuboidal annotation also helps in identifying space and volume; hence, it’s commonly used in construction and medical imaging verticals.
Apart from 2D bounding boxes, polylines are probably one of the easiest image annotation techniques. Polylines are small lines joined at vertices, which can be used to annotate line segments such as sidewalks, wires, lanes, pipelines, rail tracks, streets, etc.
Polygons are a specific way of annotating images. This is best used to annotate objects that often have an asymmetrical shape using a series of X and Y coordinates across the edges. For example, street signs, vegetation, logo images, rooftops, facial recognition, landmarks, and so on. Annotators can adjust the borders of a polygon to point out an object’s accurate shape whenever required.
This technique is used to label very specific features of the target object. You can use key point annotation to mark facial features, body parts, poses, etc., or pinpoint the location of the eyes, nose, and mouth on a human face. Key points are commonly used for security purposes since it helps CV-based models read and distinguish human faces quickly. Other use cases include biometric boarding, emotion detection, and so on.
Image annotation is the fuel that powers Computer Vision, Deep Learning algorithms, and other such AI/ML-based models. Its advantages are non-negotiable and this process is essential for industries including security, healthcare, agriculture, and autonomous vehicles to name a few. It helps machines to detect and identify images accurately, making it possible for businesses across B2B and B2C domains to automate processes and go for resource optimization. As the demand for image annotation increases, it is imperative to choose a reliable and experienced image annotation company that makes the process easier for you.