Image Annotation is a human-powered task for the annotated image with the label. These labels have been predetermined by AI engineers and chosen to provide information on the computer vision model about what is shown in the picture.
Depending on the project, the number of labels in each image can vary. Some projects will only need one label to represent the content of all images (image classification). Other projects can require many objects to be marked in one picture, each with a different label.
The image annotation is the foundation behind many AI that interact with you and is one of the most important processes in computer vision (CV). In image annotations, data labels using tags, or metadata, to identify the characteristics of the data you want your AI model to learn to recognize. This marked image is then used to train computers to identify these characteristics when presented with fresh data and not labeled.
To compile a complete list of current applications that use image annotations. For now, we will highlight some of the most attractive cases of use in all large industries including Real estate, Agriculture, Healthcare, Manufacturing, Retail, Ecommerce, Finance, and Transportation.
Types of image annotation
There are three types of popular image annotations, and which to choose for your use case will depend on the complexity of the project. With each type, of high-quality image data, used, the more accurate the AI prediction produced.
Classification
The easiest and fastest method for image annotations, the classification only applies one tag for an image. For example, you might want to see and classify a series of grocery shelves and identify which one has soda or not. This method is very suitable for capturing abstract information, such as the example above, or the time of the day, if the car is in the picture, or filtering images that do not meet the qualifications from the beginning. While the classification is the fastest image annotation in providing a single label, high level, that is also the vaguest of the three types that we highlighted because it does not show where the object is in the picture.
Object detection
With object detection, the Annotator gave a specific object that they need to label in an image. So if an image is classified as having a soda in it, this takes one step further by showing where soda is in the picture, or if you are looking specifically where the orange soda is. There are several methods used for object detection, including techniques such as:
2D Bounding Box:
The annotator applies a rectangle and box to determine the location of the target object. This is one of the most popular techniques in the field of image annotation.
3D Bounding Box:
The annotator applies a cube to the target object to determine the location and depth of the object.
Polygonal segmentation:
When the target object is asymmetrical and does not easily enter the box, the Annotator uses a complex polygon to determine its location.
Line and Splines:
Annotators identify boundaries and key curves in the picture to separate areas.
Because object detection allows overlapping in the use of boxes or lines, this method is still not the most appropriate. What it provides is the general location of the object while remaining a relatively fast annotation process.
Semantic Segmentation
Semantic segmentation Solves overlapping problems and detection of objects by ensuring that each component of the image is only owned by one class. Usually done at the pixel level, this method requires the Annotator to determine the category for each pixel. This helps teach AI models how to recognize and classify certain objects, even if they choked.
In general, the annotation of the image is difficult because of the same reason as building any challenging AI model. AI requires a large amount of high-quality data to function properly, a variety of teams to provide the data annotation and a comprehensive data pipeline to be executed. For many organizations, the time, money, and efforts needed may not be feasible. For those who do not have internal resources to complete a project, outsourcing image annotation is a valid option. They can provide image data, annotators, tools, and expertise to assist in such great efforts.
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