Nowadays, the world around us is changing really fast. Everything is so different from how it used to be a couple of years ago. And in this fast-paced environment in which organizations are trying to strive, Artificial Intelligence is booming, even crucial. And Data Annotation is a very important part of Artificial Intelligence, here’s why.
Data Annotation is the process of categorizing and labeling data for AI applications. Put simply, annotators separate the format they are looking at, and label what they see. The format can be an image, a video, audio or a text.
In this blog, we will share the different types of Data Annotation with you and we will explain the process of each type.
Annotators label specific objects in an image.
For example, the image represents a classroom. Annotators label as following: table#1, table#2, chair#1, chair#2, board, lamp, etc…
There are 6 Types of Image Annotation:
- Bounding Box Annotation: Annotators highlight the specified object in a square shape or 2-dimensional square.
- Cuboid Annotation: Annotators label the specified object in a 3-dimensional square shape, also known as a cube. This type of annotation is good to calculate the depth or distance of various objects.
- Landmark Annotation: Annotators label around the specified image with small dots. This is commonly used for recognizing faces such as unlocking a phone via face recognition for example.
- Polygon Annotation: This type of annotation is similar to the Bounding Box, but it is more accurate because the annotators can select what they want rather than drawing a square all over the object. This type of annotation is useful when dealing with aerial imaging. Using Polygon annotation, annotators can label roads, street signs, buildings, trees, etc.
- Semantic Segmentation: This type consists of separating the objects in the image by grouping them in different colored pixels. For example, to do this annotation for the image of a road, annotators segment the road in three categories. The first segment is the people (pixelated in blue), the second segment is the cars (pixelated in red), and the third segment is the street signs (pixelated in yellow). However, there is a different version of semantic segmentation called “Instance Segmentation”. The only significant difference between these two segmentation methods is that Instance Segmentation has the option to create a segment inside of a segment. This means that annotators can differentiate the people pixelated in blue by creating an inner segment naming the people “person#1 , person#2, and person#3”. Of course, person#1 would have a different pixelated color than person#2 and so on.
- Lines & Splines Annotation: The purpose of this type is to know the boundaries and lanes.
Annotators stop the video and label what they see. It is the same as Image Annotation, but with motion. Furthermore, the types of video annotation are the same as Image Annotation: Bounding Box, Cuboid Annotation, Landmark annotation, Polygon annotation, Semantic Segmentation, and Lines and Splines.
Image and Video Annotation are part of the AI field that only works on digital images and videos called Computer Vision.
Annotators label sentences or paragraphs with metadata about the selected words. Metadata means data about data or in other words information about the data used. The process is similar to highlighting specific words in an academic book. You highlight the required sentences and you write on them characteristics, but instead of writing on them, annotators label them.
There are 4 Types of Text Annotation:
- Sentiment Annotation: Annotators label the text according to the feelings they’re getting from the text. The feelings can be positive, negative, or neutral.
- Intent Annotation: Annotators label the text with the action they want, such as command, request, or conformation.
- Semantic Annotation: Annotators label the text with entities as a reference. For example: name, place, date, etc.
- Linguistic Annotation: Or Phrase Chunking. Annotators label the text with grammatical entities, such as nouns, adjectives, verbs, adverbs, etc.
Before labeling and categorizing audio clips that are different in sound, people capture unorganized data in the form of audio. For example, capturing raw data at a party. Annotators will divide the sounds into groups, as following: a sentence said by person #1, a sentence said by person # 2, music, and noise. This type of annotation is used for sound recognition and for creating a conversation between a human being and a technological device like Siri.
When we say Future, we say Artificial Intelligence, and knowing about one of the most important processes that will ensure your AI and Machine Learning projects will scale, is crucial.
Text and Audio Annotation are part of the Natural Language Processing field in AI which deals with the meaning of words.
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