AI-powered applications like automatic speech recognition augmented reality and neural machine translation are futuristic technologies that will transform lives and businesses around the globe. So are computer vision technologies like remote-controlled drones, autonomous vehicles, and facial recognition. However, none of these next-gen technologies would be possible without Image Annotation.
Image annotation is the principal force behind many Artificial Intelligence (AI) products we interact with and is one of the integral processes of Computer Vision (CV) – technology that essentially enables computers to gain high-level understanding from digital text, audio, images, or videos, to interpret visual information akin to humans. CV strives to give a machine eye – the ability to see, analyze and interpret the world.
Image annotation involves the process of labeling images of a dataset to train a machine learning model. It is the human-fueled task of annotating an image with labels. A central step in the creation of most computer vision models, image annotation is vital for datasets to be useful components of machine learning and deep learning.
In image annotation, the annotators use tags or metadata to identify the specific characteristics/ features of the data that the AI model will be trained to learn or recognize. These tagged images, popularly known as datasets, are then used to train the computers to identify and categorize these characteristics in unlabeled images.
Image annotation is used for object recognition/ detection, machine learning, image classification, image segmentation, and computer vision models. It enables the creation of reliable and exhaustive datasets for the models to train on.
Listed below are the three most used types of image annotations. The use of each depends on the complexity of the project. The more high-quality the image data used, the more accurate is the AI model’s predictions.
The easiest and quickest type of image annotation, classification, applies only one tag to an image. In this machine learning model, images use a single label to identify the entire image. It creates datasets that train the AI model to recognize specific objects even in an unlabeled image that looks similar to images in datasets used to train the model. This method is ideal for capturing abstract information, or time of the day, or for filtering images that don’t meet the criteria from the beginning. E.g., classifying a series of images of a grocery store’s shelves to identify which ones have fizz drinks or not. Training images for image classification is also referred to as tagging and aims to identify the presence of a particular object and categorize it according to a predefined class.
These models take image classification a step further to identify the presence, location, and exact number of objects in an image. The image annotation process requires boundaries to be drawn around specific objects that have to be labeled in an image. Taking forward the fizz drinks example, if an image is classified as a fizz drink, this type of image annotation goes a step further by shoeing where the fizz drink is. Or in case you are specifically looking for where a lemon soda is. There are several annotation techniques used for image annotation, including:
– 2D Bounding Boxes: One of the most popular techniques in CV, Bounding Boxes are rectangular or square boxes used to define the location of the target objects within an image.
– Cuboids/ 3D Bounding Boxes: These are cube-shaped boxes used to define the target objects’ location and depth of the object within the image.
– Polygons: Used to annotate irregular objects within an image – asymmetrical objects that don’t easily fit into a box, complex polygons are used to define their location by marking each of the vertices of the intended object and annotating its edges.
– Landmarking: Used to identify and tag central points of interest within an image, the landmark, or key points, landmarking is especially significant in face recognition.
– Lines & Splines: Significant for boundary recognition, these annotate images with straight or curved lines. Key boundary lines and curves are marked in an image to differentiate the regions. Of great value for boundary recognition to annotate sidewalks, road marks, lanes, and other boundary indicators, lines and splines also play a significant role in the safe operation of autonomous cars.
People detection is one of the most common examples of object identification. It entails the computing device to analyze frames to identify specific object features and recognize them in unlabeled images.
This type of image annotation involves dividing an image into multiple segments and is used to locate objects and boundaries in images. It is performed at the pixel level and allocates each pixel within an image to a specific object or class. Segmentation is of great value in projects requiring higher accuracy in classifying the available dataset. Image segmentation is further categorized into three:
– Semantic Segmentation: Used when great precision is required, ensures every component of an image belongs to only one class. This type requires annotators to assign categories (like a car, sign, bike, pedestrian) to each pixel. It helps teach AI models how to recognize and classify specific objects, even if they are partially hidden or obstructed.
– Instance Segmentation: This type of image annotation identifies the presence, location, number, and shape or size of specific objects within the image. It helps label every single object within an image.
– Panoptic Segmentation: This integrates both semantic and instance segmentation and provides data labeled for background and the object within an image.
The increased availability of image data for companies leveraging AI in their business operations has further led to a proportionate increase in the number of projects relying on image annotation. Designing a holistic and efficient image annotation process is increasingly becoming vital for organizations working within the realm of Machine Learning (ML). Done right, image annotation delivers high-quality training data – a critical component of any effective AI model.
We are an advanced technologies software services company with a sharp focus on Data Annotation & Labeling and AI ML Model Development & Automation. We leverage state-of-the-art data annotation & labeling tools and over 180+ skilled resources to deliver high-quality and scalable datasets customized to client requirements with a proven services portfolio across image, video, text, and audio. These help our customers train AI/ML algorithms according to specific use cases, build top-performing AI models, and accelerate deep learning. Our solutions also help overcome one of the most crucial bottlenecks in AI initiatives today – the availability of qualitative and scalable training datasets in a cost-effective model. aiTouch’s annotators work on both client and in-house platforms to deliver a versatile range of work, from labeling to ground-truth dataset creation. We work across verticals like retail, automotive, healthcare, BFSI, manufacturing, enterprise, governance, to name a few.
Want to know more about our data annotation & labeling portfolio? Get in touch. Our team will be happy to assist you.
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