Instance vs. Semantic Segmentation: What Are the Key Differences?
The applications of computer vision are limitless. Computer vision is one of the hottest subfields of artificial intelligence at the moment, ranging from self-driving vehicles to robust facial recognition software. However, human vision has proven to be an exceedingly difficult gift to bestow on machines.
As living creatures, it is natural for us to make sense of the world around us. Vision requires sophisticated deep learning algorithms on computers. However, algorithms do not operate by magic; they require massive amounts of high-quality data. This is where two-dimensional and three-dimensional semantic segmentation comes into play.
Computer vision has the potential to completely transform a variety of industries. However, it all begins with the process of object identification and classification, also known as image segmentation. Let's take a closer look at what this process looks like and how, when done correctly, it results in high-quality, reliable machine learning datasets for training models.
Instance vs. Semantic Segmentation
Any computer vision project's objective is to develop an algorithm for object detection. However, this is insufficient — accurate object detection is required. Otherwise, unmanned vehicles and autonomous vehicles would unquestionably pose a danger to the public.
Segmentation of images and videos is used for environment analysis. In a nutshell, segmentation processes visual input using a "divide and conquer" strategy.
Two types of image segmentation exist:
• Semantic segmentation. The objects depicted in an image are classified according to predefined categories. For example, a street scene might be divided into "pedestrians," "bikes," "vehicles," and "sidewalks."
•Instance segmentation. Consider instance segmentation to be a more refined implementation of semantic segmentation. For example, the category "vehicles" is subdivided into "cars," "motorcycles," and "buses" — instance segmentation identifies the instances of each category.
In other words, semantic segmentation considers multiple objects belonging to the same category to be a single entity. On the other hand, instance segmentation identifies specific objects within these categories.
To achieve the highest level of accuracy possible, computer vision teams must create a dataset for segmentation.
Semantic Segmentation for Deep Learning
Techniques for image processing have come a long way. Before the advent of deep learning, image processing relied on grey level segmentation, which was insufficiently robust for representing complex classes (for example, "pedestrians"). Conditional random fields (CRFs), a class of statistical modeling techniques, enabled structured prediction, paving the way for subsequent methods.
Deep learning enables the use of fully convolutional networks (FCNs), U-Nets, and the Tiramisu Model — as well as other sophisticated solutions capable of producing results with unprecedented resolution.
Semantic segmentation techniques are constantly improving. However, how is this technique applicable outside of the laboratory?
Semantic Segmentation in Action: Real-World Applications
The following illustrates the impact of semantic segmentation across industries:
• Self-driving automobiles. Semantic segmentation allows autonomous vehicles to avoid colliding with pedestrians, other vehicles, lanes, and other objects of interest.
• Medical examinations. Semantic segmentation is used to detect and outline tumors, abscesses, and other MRI abnormalities.
• Imagery from satellites. Semantic segmentation creates an aerial map of the world, delineating bodies of water, roads, crop fields, and even available parking spaces.
• Clothing. Segmentation is used by fashion retailers to digitally recommend similar items of clothing and swap outfits.
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Are you looking for high-quality training data for your upcoming machine learning project? Contact a member of our team today to schedule your complimentary demo.
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