Emily Clarke

What is Semantic Segmentation?

Machine learning and computer vision require training to define objects that appear in an image or on video. Whether the image source is a piece of video from an autonomous vehicle that needs to detect objects to maneuver safely or from a security camera that watches over a large and sprawling construction site, every pixel of the image needs to be accounted for by computer vision to accurately categorize the objects in view.

To do this, semantic segmentation is used. This is the process of segmenting an image down to the pixel level and then classifying the pixels. Other types of classification rely on assigning classes to an entire image, but a semantic segment is much more granular. To understand images, annotations need to be added, and many images need to be used to create masked images that are understood by the machine learning model.

Where Are Semantic Segments Used?

Semantic segments are used for operations that require a lot of detail and control. Autonomous vehicles can use this type of segmentation to understand the difference between pedestrians, street signs and other objects they encounter while in operation.

This approach may also be used in medical imaging to examine tissue for abnormalities. When medical images are classified by pixel, single images can be broken into many parts, providing a very detailed view. This may lead to a more accurate diagnosis of illness and injury.

The Difference Between Semantic and Instance Segmentation

Instance segmentation differs from semantic segments in that instance segmentation is used to identify individual entities in an image. A semantic segment identifies objects according to class. An example of this would be when an image of a street scene shows numerous people standing around.

A semantic segment would identify the pedestrians simply as pedestrians. It would essentially identify the category of pedestrians, but it would not differentiate between the different pedestrians. Instance segmentation would identify the individual pedestrians, giving each a unique presence on screen. A semantic segment may be better suited for analyzing an image to determine the magnitude of a group, while instance segmentation may be better for capturing the number of a group.

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Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at learning platform blog.