Image Annotation for Computer Vision: Best Practices

We’ve all heard about the self-driving cars launched by renowned car manufacturers - Tesla, BMW, or Audi. But, only a few know that these cars operate using computer vision models that use multiple cameras and ultrasonic sensors to capture images in real-time and help the vehicle identify objects, signs, or signals for safe driving.

The application of computer vision isn't limited to self-driving vehicles. It is applied across numerous sectors and industries, including healthcare, agriculture, construction, and manufacturing, where it elevates humans’ lifestyle, simplifies complex tasks, and makes living easier. But, how do computer vision models operate well enough to process and identify real-life elements so quickly?

The answer lies in image annotation. It uses deep learning algorithms to label and tag objects in a given image for easy detection by computer vision models. So, let’s know more about image annotation, its type, and the techniques it uses to train machines and other computer-assisted applications.

Image Annotation: An Overview

In layman’s language, image annotation gives machines the power to see, recognize and interpret objects in an image. Human annotators label and tag different objects in an image via bounding boxes, lines, polygons, and other techniques. These tagged images are organized in a dataset and fed to a machine learning algorithm so that they can learn to replicate the results.

Here, cars and road signs have been highlighted with the 3D bounding box technique to train AI-enabled and other computer-assisted apps for object location. In other words, annotators add labels to images that they want their ML algorithm to recognize and get trained in.

Applications Of Image Annotation

From shopping and security to driving, machine learning and artificial intelligence models rule day-to-day lives. For instance, several cosmetic online stores use virtual try-on technology to detect a buyer's face, lips, or cheeks so that they can see how a particular shade of lipstick, nail paint, or any other cosmetic item will look.

AI/Ml models are trained with relevant datasets via image annotation, applied across diverse sectors, such as:

• Agriculture
• Banking and insurance
• Manufacturing
• Healthcare
• Robotics
• Sports analytics
• Medical imaging
• Fashion and retail

Image Annotation Techniques

To train machine models with relevant datasets, images can be annotated using various techniques. These include:

2D Bounding Boxes

Annotators draw rectangular boxes (from one corner to the other) around the objects of interest for easy recognition by computer vision models.

3D Bounding Boxes/Cuboids

3-Dimensional boxes are drawn and labeled around the objects to bring out the depth. While 2D accounts for length/breadth, 3D annotates the depth/height as well.

Polyline, Line & Spline

Straight and curved lines are drawn around objects to help autonomous vehicles in detecting lanes, edges, sidewalks, and other boundary indicators.

Polygons

Polygons are used to annotate, label, and capture irregularly shaped objects in an image. It allows pixel-perfect annotation for effective recognition.

Landmark

Specific key points are labeled to identify gestures, facial expressions, emotions, movements, poses, and other types of objects of interest.

Types Of Image Annotation

Image Classification

Also known as tagging, it is one of the most basic and simple annotation techniques. Under classification, the entire image is annotated with a single label.

Let’s consider an example where an annotator has to label different types of birds in a set of images. The annotator will be provided with predefined labels, like a crow, eagle, sparrow, etc., and asked to classify each image with a label as per the specific type. The images can have other elements as well (like trees, pastures, other animals, roads, and lakes) but the annotator will not label those.

The objective of image classification is to only identify the object belonging to the predefined class (in this case, cars.) It helps machine learning models trace the presence of a similar object (category) in a dataset. These annotated images will further train machine learning models in memorizing the characteristics of each kind of bird and identifying them in other images.

Object Detection/Recognition

An essential aspect of annotation, object detection, or recognition helps machines detect the object(s) of interest in a dataset. That object could belong to more than one class. For instance, a standard scenic view can have elements like rivers, mountains, houses, or people. All of these can be annotated separately within the same image.

Annotators draw boundaries and add metadata to help machines recognize the type, location, or number of object(s). Multiple objects can be labeled in a single image via diverse detection techniques, such as polygons, lines, splines, or bounding boxes. This data is fed to machines for easy recognition and information storage for future reference.

Image Segmentation

Image segmentation is one of the most advanced image annotation practices. While classification and detection annotate every object in an image, segmentation annotates every pixel, where each pixel is allocated to a specific object or class. It is preferred in cases where higher accuracy and precise results are required.

Segmentation can be of two types.

Semantic segmentation: Images are segmented according to a particular class type. For a street, this means segmenting vehicles and allocating pixels to a similar type of class, e.g., “trucks" or “heavy load vehicles." A similar process can be followed for other vehicle types. This, in turn, makes it easier for AI modes to comprehend images.

Instance segmentation: An extended version of semantic segmentation where the number of instances are labeled. To be precise, every single truck in the image will be labeled as Truck 1, Truck 2, and so on.

Image Transcription

Besides objects, images contain text that must be extracted, digitized, and processed by machine learning models. This type of image annotation is commonly implemented in OCR (optical character recognition) systems and other similar models. It also has potential usage in self-driving cars, to help the vehicles read signs, directions, etc.

Image transcription is a necessary appendage to all other types of image annotation. For instance, while we can train an AI/ML model to identify road signs, vehicle number plates, invoices, or prescriptions, the other annotation techniques won’t be able to teach the model to understand what is written on the sign or the prescription. They have text that can’t be processed by machines. Once these are transcribed via captioning and labeling, it becomes easy for the machine learning algorithms to extract the text and comprehend it for diverse purposes.

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Annotate your images to train machine learning algorithms for easy object detection and identification. Regardless of the industry or vertical, image annotation improves the performance of ML/AI-enabled applications and achieves desired outcomes.

You could either use various annotation tools or outsource image annotation services to professionals to save time, money, and other critical resources. If you need more information about image annotation services or need one for your project, get in touch with experts at [email protected].