Image annotation is the process of technically affixing digital labels to an image or a series of images. This process varies from one label for an entire image or multiple labels for every cluster of pixels within that image, and varies in annotation type. A straightforward example of this is equipping annotators with images of creatures and digitally labeling each image with an accurate animal name. The labeling method relies on the image annotations used for the notation task. Correctly confirmed annotated images are usually referred to as "ground truth data", or a set of referencing annotations that can be used within a computer vision algorithm. Through uniform and accurate digital annotation training, the computer modeling exercise can, for example, accurately distinguish animals from unannotated images with a high level of confidence.
Image annotation is the basis and a requirement behind numerous commercial Artificial Intelligence (AI) products on the market, and it is one of the crucial processes in Computer Vision (CV). In image annotation, digital data labelers use an image and its metadata, to identify the key characteristics of the data that an AI model will learn to recognize. These digitally tagged images are the foundation that is used to systematically “train” the computer system to correctly identify key features when new and unlabeled data are shown.
For the training to be practical and beneficial, computer systems require numerous examples so as to learn how to categorize items correctly. Furthermore, with the improved availability of data images for businesses that are growing and nurturing their AI models and systems, projects depending on image annotation have risen exponentially. As a result, building a comprehensive, effective image annotation methodology has become vital for organizations working within this area of machine learning (ML).
Image Annotation Application
Many current applications leverage image annotation and the most influential use cases spanning the major industries are, as example, as follows:
Manufacturing businesses utilize image annotation to provide real-time information about their inventory levels within their warehouses. Trained computer models can evaluate stock image data to decide if or when a product might soon be out-of-stock and needs replenishing. In addition, specific manufacturers use image annotation to monitor key infrastructure elements within their plants. Teams digitally label images of their vital equipment components, information which is then employed to instruct computer modeling systems to recognize and identify explicit faults or failures, thus leading to more immediate hardware corrections and providing more acceptable maintenance overall.
Health and Healthcare
Medical personnel is augmenting their diagnoses with AI-powered resolutions. For instance, AI can readily examine radiological images to identify the probability of foreign objects being present. In one instance, medical teams train a computer model using a multitude of radiology scans labeled with both cancerous and non-cancerous zones until the machine model can accurately learn to differentiate them on its own.
It is important to note that AI is not intended to substitute for trained and specialized medical advice, but it can be used to add accuracy to critical health determinations.
The agriculture industry utilizes AI, video or image based, for a myriad of benefits, such as:
Estimating future crop yield,
Evaluating soil content, and
Planning for future agricultural expansion
Developing autonomous vehicles & machinery,
One farming business annotates still shot digital images to distinguish between weeds and crops – right down to the pixel level. This annotated imagery is then used to apply chemical pesticides to those areas only where weeds are growing, rather than spraying onto the entire field. This process reduces chemical weed spraying, saving significant amounts of money on pesticides every year.
Finance and FinTech
Banking and finance companies use facial recognition technology to verify the identity of their customers withdrawing money from their ATMs. This is accomplished through what is called a pose points image annotation process, which digitally maps key facial features such as eyes, nose, and mouth. Consequently, facial recognition presents a more direct and precise method of defining identity, reducing the prospect of fraud.
Image annotation is required to build a computer modeling system that can examine an entire product catalog and administer the end user's results. Retailers are also piloting image annotative systems within their stores. These systems periodically scan and manage digital images of product shelves to decide if a product is close to running out-of-stock, revealing that it requires reordering. These systems can also check and scan barcode images to collect product information using what is known as image classification, which is a key method used for digital image annotation – which will be discussed further below.
Image Object Detection
With image object detection, annotators are provided with specific entities that they must digitally label within an image. So if an image is categorized as having a car within it, this process takes it a step further by indicating where the vehicle is within the digital image. Several methods are used for image object detection, including procedures such as:
* 2-D Bounding Boxes: Human annotators are provided with an image for bounding box annotation and are then tasked with drawing a box around certain objects within the image for labeling.
* 3-D Bounding Boxes: 3D cuboid annotation is where annotators are tasked with drawing a box around image objects.
* Polygonal Segmentation Boxes: Using polygons, annotators are able to draw lines by placing concentric dots around the outer edges of the object they are annotating.
* Lines, Splines, and Rhomboids: Lines and splines are used for a mixture of purposes, but they are mainly used to train computers to recognize boundaries and lanes.
* Semantic segmentation: Semantic segmentation is the process of annotating every individual pixel in a complete image with a digital tag.
Because image object detection permits overlap in digital boxes or lines, this technique is still not the most precise. However, it does provide the object's general zone while still being a moderately quick image annotation process.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
Why is Image Annotation so Important?
Companies can build and improve their AI digital implementations using high-quality, human-powered data image annotation. The outcome is an enhanced customer experience solution that can provide informed product suggestions, suitable or relevant search engine results, sensible speech recognition, and more.
Image annotation is currently considered one of the most paramount responsibilities and obligations a computer system has within the digital age, as it allows us to analyze and diagnose our world through an optical lens, providing a digital perspective. As a result, image annotation is essential for many business applications, including computer vision, facial recognition, robotic vision, and other solutions that depend on machine learning to interpret digital images correctly.
Key metadata must be assigned to the images via identifiers, captions, or keywords to successfully train these computer modeling solutions. From computer vision methods used by quite modern self-driving vehicles and apparatuses that pick, sort and pack produce to healthcare business applications that can automatically identify medical conditions; many such use cases require a high volume of annotated images. Image annotation improves both accuracy and precision by virtually “training” these systems. Without this key concept, models cannot be trained properly or correctly.
Business process automation has consistently been a pivotal pillar of digital transformation ambitions and initiatives. However, the increasing demand for digital annotation solutions and rapid shifts in business operating environments have resurrected the urgency of digitalization. Enterprises face challenges to transform their customer experiences, automate their business processes, and optimize costs, which will only lead to further increased business process investments – and this is where image annotation will be the cornerstone of such initiatives.
Different Image Annotation Types
As described earlier, image annotation is the process of annotating target objects within a digital image’s region of interest. This is performed to train a machine to recognize objects under the same classes in unseen images and visual scenes. However, this method can be quite challenging. That’s because there are different approaches to developing deep learning model architectures and techniques for training a machine to do this.
This means we should learn about today’s most frequently used image annotation types and methods. Here they are:
1. Bounding Boxes
This is a simple yet versatile type of image annotation. And this is the primary reason why this method is among the most widely used techniques for annotating images in a dataset for a computer vision application’s deep learning model. As its name implies, objects of interest are enclosed in bounding boxes. An image is annotated with markers for X and Y coordinates, which are the top left column and the bottom right row of the bounding box that encloses the object of interest.
2. Semantic Segmentation
This image annotation method is where each pixel in an image is assigned with a particular semantic concept label. The image is initially marked, with the objective being to separate it as individual regions. These are annotated with different semantic labels, i.e. Each pixel in a certain region is assigned “road”, while another set of pixels in a different region is annotated with the concept label “sky”.
3. Polygonal Segmentation
Complex polygons are used in place of simple bounding boxes for this image annotation method. This is known to increase model accuracy, in terms of finding the locations of objects within a region of interest in the image. In turn, this is also known to improve object classification accuracy. That’s because this technique cleans up and removes the noise around the object of interest, which is the set of unnecessary pixels around the object that tends to confuse classifiers.
4. Line Annotation
Lines and splines are used for this image annotation method to mark the boundaries of a region of interest within an image that contains the target object. This is often used when regions of interest containing target objects are too thin or too small for bounding boxes.
5. 3D Cuboids
This is an image annotation method that’s commonly used for target objects in 3D scenes and photos. As its name implies, the difference between this method and bounding boxes is that annotations for this technique include depth, and not just height and width.
6. Landmark Annotation
Also known as dot annotation, this method uses dots as annotations around target objects, which are enclosed by the image’s individual regions of interest. This is frequently used for finding and classifying target objects surrounded or containing much smaller objects. Plus, this is often used to mark the outline of the target object.
These are the different image annotation types and methods that are commonly used today. Datasets of digital images for the deep learning models of computer vision applications are annotated through these techniques. The method that must be used should match the architecture of the deep learning model and the use case for the computer vision tool. Any of these image annotation tasks can consume a lot of time and resources. This is where Kili can help because specially designed image annotation tools like Kili Playground offer multi-purpose benefits for data scientists, AI researchers, and machine learning developers.