What is Annotation Ontology

Machine learning and computer vision applications require substantial training before deployment. Systems must learn to understand and recognize what they're looking at before reacting and executing functions. Whether in a healthcare setting or a warehouse, AI systems must understand the context surrounding the objects they see.

That's where ontology comes in. Ontologies provide more than top-level visual information about an object. They offer more detailed conceptual information, such as the relationships one object has to another or how it's represented in data. Also known as taxonomies or labeling protocols, ontologies play a big part in allowing you to semantically program computer vision and machine learning models to understand the complexities of the data they view.

It's what makes these intelligent systems capable of understanding complex information similarly to the human brain.

How Annotation Ontology Works

Think of how you recognize objects in your everyday life. If you see a dog walking on the street, you can easily define it as such. But you use tons of semantic information to get there. For example, you know how a dog relates to a cat, allowing you to differentiate the two animals. You can also use semantic information to see if that dog is a stray, separated from its owner or aggressive. All that information combines to help you learn about the dog you see. It's about using observations and drawing inferences from everything you see.

Computer vision and machine learning models need the same deep level of understanding to perform efficiently.

In annotation, ontologies are hierarchical structures that capture different levels of information. They allow for fine-grain differentiation and can provide more detailed annotations. It goes beyond top-level descriptors, including nested attributes to provide a more comprehensive understanding of the target object.

At the top of the hierarchy are classes or categories. They represent the highest level of concepts you want to express. Below that are nested classifications that go deeper into the object's attributes, for example, a top-level class could identify an object as a dog. Nested categories can then differentiate things like color, the presence of a collar, movement, speed, etc.

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