How Image Annotation Shapes Artificial Intelligence Models

Thousands to millions of annotated images are used to train machines, robots, and computers to recognize various types of objects and its attributes. Now you may wonder, how does image annotation help make an object meaningful to a machine? There are several companies such as oworkers that offer image annotation services to shape AI-based models through high-accuracy human-annotated data.

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These are a very important aspect of the behind-the-scenes activity and innovative product design. These techniques are used in various industries such as in retail, e-commerce, facial and voice recognition, cashier-less shopping, self-driving cars, agriculture, manufacturing, energy, healthcare, robotics, drone development, speech recognition, virtual assistants like Alexa and Siri, and could be used in so much more.

What exactly is image annotation, and why is it so important?

Let’s look at this all in terms of how a human learns to recognize objects. When children are learning to recognize and interact with the world around them, there is a parent or someone there to teach them. If that person continuously tells a child that the family dog is an apple, the child will eventually begin to call the dog an apple.

Or, if every time the child sees a picture of a circle, the parent or teacher tells the child it is a square, the child will quickly begin to refer to every circle as a square. The same is true in artificial intelligence models.

In order for a computerized device to decipher between your face and the face of a stranger, or for a self-driving car to determine what objects are in its path, or for an online shopper to search for the right carton of milk, it must first be programmed to “learn” to decipher differences in images. For this to happen, cvat image annotation must take place. It is a task that requires a great deal of manual work, patience, and skill in accurately labeling and classifying these images and its attributes.

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Image annotation is the task of annotating an image with labels. These labels are very important, since they are what make it possible for machine learning with AI models. The labels are selected to communicate with the computer about vision model information of what is being shown in an image. Within the process of properly annotating or labeling images, there are many complicated steps and terms that these experts understand and put into practice every day.

Terms and Types of Annotations and How They Are Used

Bounding Box Annotation

Bounding boxes are the most commonly used type of annotation. A bounding box is a rectangular box that is used to define the location of a target object determined by the x and the y axis coordinates.

This type of annotation is mainly used for tagging the damaged motor vehicles parts, sports analytics or various other objects that need to be recognized or classified by computers. It is one of the most common and important methods of image annotation techniques mainly used to outline the object in the image.

Polygon Annotation

There is also polygonal segmentation for objects that are not a perfect rectangular shape to define an object with greater precision.

3D Cuboid Annotation

3D cuboids are similar to bounding boxes but add more depth information about an object.

This is also called 3D cuboid annotation that involves, high-quality labeling and marking technique to highlight the objects in the third-dimension sketching formats. It helps to calculate the depth or distance of various objects like gadgets, buildings, vehicles and also on humans to distinguish the volume and space of the object. 3D cuboid annotation is basically used for construction and building structure fields including radiology imaging in medical fields.

Lines and Splines Annotation

Lines and splines identify images such as roadways, sidewalks, and different road lines such as broken lines, continuous lines, yellow and white lines.

Semantic Segmentation

Semantic segmentation annotates pixels in an image to assign them a class. A class could be defined as a pedestrian, a car, or another object.

Points and Landmark Annotation

An experienced annotator will also understand how to label key-points and landmarks which are used to detect small objects and shape variations by creating dots across the image. This type is used in facial recognition to detect features, expressions, emotions, and human body parts, movements and poses.

Many wrongly assume that outsourcing annotation services is too costly. The truth is that more money is often lost due to poor quality data as a result of attempting to do it all in house. Outsourcing these services helps lower costs and ensure maximum productivity with unmatched accuracy standards. Professional annotation companies work with their individual clients to fully understand and tailor fit their specific needs.

The task of handling all of this in house is a thought that could sideline any company before they even get their site or product idea launched. That is why image annotation outsourcing is so important in today’s changing ways of doing business.

Interesting related article: “What is AI?