One of the most important subfields of AI is computer vision. It is transforming a wide range of industries through its applications, from medical diagnostics to deploying autonomous vehicles, monitoring soil, crop harvesting, etc.
So, how does computer vision function?
Thanks to Annotation Process!
For a computer to see the world, analyse and draw conclusions, it needs training data. And that's where image annotation, the most important aspect of computer vision, comes into play.
With the power of annotation, the raw visual data is labeled appropriately to create training data sets for AI models using various annotation types such as bounding box, 3d cuboids, semantic, polygon, and so on.
What is Image Annotation?
Image annotation is the process of labeling essential elements of images, performed manually or with AI software to make the labeling process faster and more precise.
As AI needs the training to produce results, machine learning & computer vision demands human intervention to make the learning efficient. Hence, image annotation helps create a substantial amount of data to train computer vision & machine learning algorithms so that they can recognize accurate attributes.
Data labellers in image annotation use tags, or metadata, to identify characteristics of the data that you want your AI/ML model to learn post which these annotated images are then used to train the application to recognize those characteristics when presented with new data that hasn't been labeled.
Different Types of Image Annotation Used for Computer Vision
Bounding Boxes - In computer vision, bounding boxes are one of the most commonly used types of image annotation. It is primarily used to outline the objects in the image, typically represented by rectangular box coordinates. The bounding Boxes technique is commonly used in localization and object detection tasks.
Polygon Annotation -This technique removes the woes of annotating objects which have irregular shape. As all Objects cannot fit into a rectangular or squared boxes, complex polygons are used to define more lines and angles of the object.
Semantic Segmentation - Semantic segmentation, also known as pixel-level labeling, is associating every pixel in an entire image with a tag. When following this method, annotators are given pre-determined tags to choose from with which they must tag everything on the page. For example, consider an autonomous vehicle that must be distinguished between the road and other objects, such as the traffic light. Semantic segmentation is used to differentiate between the predefined regions, which are road and traffic lights.
3D cuboids - Much like bounding boxes, 3D cuboid annotation tasks annotators with drawing a box around objects in an image. Where bounding boxes only depicted length and width, 3D cuboids provide additional depth information about the target object as we get a 3D representation of the target object where in the computer vision system can learn to distinguish features like volume and position.
Landmark Annotation - This type of annotation method is also referred to as dot annotation. As in this method, dots are created across the image to identify shape variations and small objects. This annotation is useful for face recognition, and by tracking multiple landmarks, we can easily recognize facial features and emotions.
What industry needs Image Annotation
- E-Commerce- Image Annotation helps maintain a searchable product database, thus enabling a swift shopping experience. AI is taking the retail and e-commerce industry to different levels to provide a much better shopping experience. However, this can only be possible when computer vision-based algorithms are trained with accurate data sets. Image annotation can train the computer vision-based algorithms to recognize clothes, shoes, bags, accessories, etc., which will not only help manage catalogs systematically but also provide a swift shopping experience.
- Medical AI- Image Annotation can help in diagnosing a disease by labeling medical imaging data The shift in the technology world has allowed computer vision to improve the accuracy of treatment & diagnosis, reduce backlogs, and cut down patient wait times in the Healthcare world. For diagnosis and treatment, healthcare professionals rely on visual data such as MRI, X-RAY scans, etc. Image annotation can train, develop, and optimize Computer Vision systems to identify patterns for diagnosis, such as fractures, tumors, etc.
- Transportation- Manually labeled images of a car's environment data can help in deploying autonomous vehicles with confidence. The drastic revolution in the automobile industry has made the transportation industry dependent on image annotation services. To deploy autonomous vehicles with confidence, the machine learning algorithms must be notably robust. Image annotation powers driverless cars by providing accurate training data of the car's environment to Computer Vision-based machine learning systems.
- Agriculture- Image Annotation can help in monitoring soil, crop harvesting, or yield analysis. Image annotation can help precision agriculture which uses robots, drones, GPS sensors, and autonomous vehicles to advance farming processes. Just like professional farmers, Image annotation can train machine learning algorithms & computer vision systems to predict plant health, crop yields, and much more.
Why Human-Powered Image Annotation is the Key
There are two technical approaches to annotating images: automated data annotation and manual data annotation as a result, choosing which one is more important.
Although automated image annotation speeds up data labelling and image delivery, it fails when data structures change frequently. Also, machines, on the other hand, do not adjust to changing demands and rules as quickly as humans do, resulting in lower quality scores.
As a result, when labelling data for AI/ML models, manual image annotation is required to ensure quality and accuracy. Furthermore, because automatic annotation machines are programmed to produce specific quantities, they are unsuitable for unsupervised machine learning or meeting additional requests.
Despite automation, human-powered annotation plays vital role in overcoming the obstacles listed above. Humans, unlike machines, can recognise new objects and identify similar objects for machine learning models without supervision. Furthermore, because the manual process is not designed to produce fixed quantities but rather to deliver customised data needs, manual image annotation can meet any additional requirements.
Make image annotation easier by choosing to work with a Partner
To function appropriately, AI requires large amounts of high-quality data sets and a diverse team of annotators to annotate that data.
For example, while dealing with image annotations, the images often come with a whole host of problems like the image may have poor lighting, the target object may be occluded, or parts of the image may be unrecognizable.
As a result, organisations may find themselves unable to devote the necessary time, money, and effort. Working with an experienced extended partner is thus the best way to complete an end-to-end image annotation project because it eliminates all of the hassles and focuses on providing you with quality datasets.
Why work with a partner for image annotation
Higher Quality Datasets
Trained Data Annotators
With so many sensitive factors at play, your data annotation partner would ensure that the final data you receive is flawless and can be directly fed into your AI model for training purposes.
Annotate precisely to make your machine learning algorithms work wonders with the best in the business.
Emerging technology like Computer vision has the potential to transform our lives. But its performance is only as proficient as its training data as the AI/ML will only learn what it is taught. At V2, we understand the importance of the training data sets and are committed to optimizing them with pixel-perfect annotation services.
With over 15+ years of experience, we are helping our clients across the globe to reach new heights and avail high-quality and customized image annotation services for their AI/ML models. We offer end-to-end services in data labeling, tagging, and annotations which are designed to address the needs of diverse industries.
Whatever your annotation needs may be, our team of experts are ready to assist you in deploying and maintaining your AI and ML projects with quality annotated data sets.
Providing Quality training data sets for face recognition systems
Learn how our team of skilled annotators took away client’s woes of setting up a new team.
Helping client in digitalizing retail industry with pixel perfect annotations
Discover how we helped our client in enhancing their platform to digitalize shopping experiences of their client’s end customers.