Image annotation is the process of labeling or classifying images using annotation tools to show the set of features that you want your model to recognize on its own.
Image annotation is a critical foundation of Artificial Intelligence. It is often used in supervised machine learning, especially in computer vision, for training data sets to help machines understand and recognize the input data.
It is worth noting that computer vision aids in establishing several critical applications like self-driving cars, facial recognition software, tumor detection software used in the medical field, and uncrewed aerial vehicles commonly referred to as drones.
Why is Image Annotation Important?
Precise image annotation lays the groundwork for training, achieving tasks, and setting goals in computer vision. It is well known that image annotation is a human-driven process with minimal computer input. It sets the standards the machine learning models try to reproduce or recreate. This means that the machine learning models will reproduce what is put in the labels precisely as it is.
How Exactly Does Image Annotation Work?
There as essentially three fundamental elements needed for image annotation to be complete. That is, quality data, an annotation tool or platform to use, and a trained individual to utilize the tool and annotate the images. The human element helps guide the image annotation process by literally labeling or classifying an image as applicable to the project at hand.
As with anything under the sun, a few challenges may arise in the process of image annotation. Since it is a human-driven process and operates efficiently, individuals performing the annotation should be trained on the specifications and guidelines to be met. Equally, the annotation tool of choice should deliver quality results, paying keen attention to the modality of the data, the type of annotation required, and the format in which annotations are to be stored.
How to Optimize Image Annotation
We have established that image annotation is essential to most Artificial Intelligence developmental cycles. Depending on the size of the company or organization, finding the correct data and assembling a competent annotation team can be a consuming distraction.
This is especially the case when the company employees are not exceptionally trained in image annotation. In such cases, it would be advisable for the organization to outsource the workforce to carry out annotation. Numerous companies have been equipped with the relevant workforce for image annotation.
Types of Image Annotation
There are different techniques employed in image annotation. Some commonly used include:
Semantic Segmentation
This technique involves dividing a given image into multiple portions. This technique is used to locate objects and boundaries, such as curves. This annotation type also achieves a higher degree of clarity since an outlining tool is used to highlight the precise shape of a particular object. It takes up more time, but the detailed accuracy is worth the hassle.
Object Recognition/Detection
Object recognition is a type of image annotation that identifies the existence, location, and quantity of one or more items in a picture and labels them appropriately. It can also be used to designate a single entity. You can train a machine learning model to recognize items in unlabeled photographs by repeating this procedure with multiple images.
Object recognition-compatible approaches, like as bounding boxes and polygons, can be used to classify distinct items inside a single image.
Image Classification
Using a sequence of images, an image classification algorithm attempts to determine whether any of the images depict the same or related things. An unlabeled image that appears like an object in other labeled images is used to train a machine to recognize that object. Tagging refers to the process of preparing images for classification. High-level classification is applied to the entire image.
Image Annotation Shapes
2D and 3D Bounding Boxes
They are drawn around the image and are usually needed in cases where the box denotes boundaries.
Lines and Splines
These label straight or curved lines on images such as roads.
Polygons
They are mainly used on irregularly shaped objects and are more precise than lines.
Considerations before Deciding to Outsource Image Annotation Services
The process of outsourcing image annotation services is crucial for any given company. Below are a few core factors that are important to consider before leaping to outsource image annotation services;
- Cost factor: The image annotation services should be affordable and not bankrupt you.
- Demonstrations: These will provide you with insight into the company’s past work. You can learn more about the company’s values by visiting its website and reviewing its portfolio. Additionally, you can check client testimonials to determine what others have to say about the organization and its services.
Ultimately, in your selection, you will require an image automation service that is reliable, known to deliver quality, and easy to integrate into your organization in terms of customization and specificity of the quality of standard required.
Also Read: 7 Common Challenges In Adopting Machine Learning For Business
