What Are the Types and Challenges of Image Annotation Used for AI/ML

As AI and ML models solely depend on data, one of the challenges faced is the fact that establishing comprehension with raw data is difficult. Since the effectiveness of AI greatly depends on the quality of the data utilized, it is vital to first transform raw data into the labeled form before moving on to further processing.

 

Image annotation bridges the gap between raw data and AI learning. It is one of the most important annotation techniques to help machines obtain high-level knowledge from digital images. This technique allows machines to interpret information quite similarly to humans. 

 

Therefore, to build successful AI models, it is important to understand the image annotation process, its types, and challenges.

What is Image Annotation?

An image has elements and entities that can be used to train machines. However, to help machines recognize objects and boundaries, data processing is done and is labeled through the image annotation process. 

 

It takes significant data to train, validate, and test a machine learning model to achieve the desired outcome. The image annotation process makes data usable to train machines by adding metadata to a dataset. This involves labeling an image to help machines understand them. It gives the computer vision model information about the objects in an image. For example, by giving labels to humans, poles, cars, etc., in a traffic image, machines will learn to identify these elements in other raw data.

 

Different types of image annotation techniques are used to label elements of an image.

Five Most Common Types of Image Annotations

Listed below are the different types of image annotation techniques:

 

1. Bounding Box

This annotation entails sketching a sizable box around different elements in an image. This type is commonly used in the automobile industry for vehicle development. In this process, developers feed the machine learning model with the bounding-box-annotated images. Bounding boxes are drawn that cover objects like vehicles, people, cyclists, and pedestrians in a traffic image. Annotators must ensure to keep these bounding boxes as close to the object as possible. Such labeling will help autonomous vehicles distinguish these entities in real-time and avoid contact with them.

 

1. 3D Cuboids

This requires annotators to draw a box around the target object with anchor points on every edge of the object. For multidimensional photos, cuboid annotations are utilized. 3D cuboids label length, width, and approximate depth. Thus, this annotation provides a more in-depth look at the different aspects of 2D and 3D objects. 

 

However, if any of the object’s edges are not clear or are being blocked by another element in the image, the expert speculates where the edge could possibly be depending upon the height and size of the object as well as the angle of the image.

 

1. Splines and Lines

Although lines and splines have many uses, their principal function is to teach machines to identify lanes and boundaries. In this process, experts draw lines around the boundaries they want the machines to understand. 

 

For example, this type trains warehouse robots to accurately place boxes in a row or items on a conveyor belt. Here, lines and splines are used to highlight the pathway that the robots have to follow. 

 

1. Semantic Segmentation

The semantic segmentation method involves assigning a tag to each pixel in a picture. For projects involving semantic segmentation, human annotators are often provided with predetermined tags to label every element of the image. 

 

This process is also used in medical imaging, where annotators are given an image of a person and instructed to label each body part with the appropriate body part tags. This type can also be used with CT scan images to tag brain lesions.

 

1. Polygons

As objects differ in size and shape, many of them can fail to fit in any bounding box or a 3D cuboid. This technique is used when developers want precise work. In such a case, polygon image annotation is used, where annotators make lines by putting dots around the object's outside border. Further, a specified set of classes, such as vehicles, bicycles, and trucks, are used to label the space inside the region encompassed by the dots.

 

What Are the Challenges Faced in the Image Annotation Process?

This technique is complicated and takes a lot of time and effort due to the numerous issues that arise during the process. Some of the common challenges faced during the process are:- 

1. Identifying different objects

One of the significant challenges faced by annotators is labeling different objects that might overlap each other. A bigger object can hide another neighboring object, making it challenging to create an edge for that element. Annotators find it difficult to segregate the images and use the image annotation process. 

 

1. Unwanted elements in an image

The input data might contain noise or other factors the system hasn’t been trained to account for. This is ultimately identified as a false pattern, thus, leading to misclassification. This increases the processing time and burdens annotators, who will need to remove the noises to make the image clear and carry out the image annotation process.

1. Consistency of the Data

Data consistency gets hampered because the annotators have different beliefs and personal and cultural biases. For example, if annotators are to label a youtube video as “funny,” labelers' personal biases may peep into whether they find the video funny or not. As machine learning algorithms depend on reliable, high-quality data to make accurate predictions, using inconsistent data might result in inconsistent results from the AI models. 

 

1. Choosing the Tool Wisely

There are several systems and tools for picture annotation, and each one offers unique features. It can be challenging to select the best tool. Furthermore, choosing an appropriate tool to fit your employees' skill sets might also be difficult. However, if you hire image annotation service providers or outsource the task, you don’t have to worry about the tool as they have access to the latest tools and software, helping them yield the best results in the shortest time. 

 

Conclusion

The performance of an AI project is directly impacted by the pictures used to develop, verify, and test the algorithms. Hence, the images in the datasets are important. To use these images properly, an image annotation process is used, which includes labeling every element in the image. This data is then used to teach an AI model how to operate. Although it is a necessary process, it is also a complex one. It has many challenges that must be resolved to get the desired result. One way businesses can resolve these issues and get high-quality data is to outsource video annotation services and image annotation services.