# Learn Computer Vision: What is Hyberbolic Image Segmentatioan?

Last updated on December 5th, 2023 at 07:29 am

# Learn Computer Vision: What is Hyberbolic Image Segmentation?

Currently, we carry out optimization at the pixel level in Euclidean embedding spaces for segmenting images. We do this through linear hyperplanes (2D visualization). Keep reading to clear your concept for the Artificial Intelligence Course regarding a key alternative for image segmentation that is done in hyperbolic space.

Computer Vision is one of the most exciting topics covered in Artificial Intelligence and Machine Learning Certification. This field allows systems and computers to retrieve important information from digital visuals like images and videos. Based on the received data, computers process information to make suggestions to the user.

If we were to simplify the concept, computer vision tries to make computers view images and videos the way humans do. Today, the advancement in deep learning and neural networks has made these systems exceed human performances in some aspects like object detection.

Today, spherical and Euclidean embeddings dominate the most-used tasks of computer vision like image retrieval and image classification.

On the other hand, Hyperbolic Image Segmentation is one of the latest standards for segmenting images. It offers multiple practical benefits like:

• Uncertainty estimation
• Boundary information
• Zero-label generalization
• Increased performance in embeddings of low-dimension

## Why do we use Hyperbolic Image Embeddings?

In Natural Language Processing (NLP) tasks, hierarchies are ubiquitous. The widespread presence of these tasks motivates the use of hyperbolic spaces in this field. This is because hyperbolic spaces inherently embed tree graphs and other types of hierarchies with minimum distortion.

While retrieving an image, you will notice that an overview picture of something can be mapped to the closeups of many unrelated pictures. These pictures might have a wide range of dissimilarities in their details.

Furthermore, let’s consider classification tasks. For such tasks, an image that contains representations from many classes is generally connected to images that possess the representatives of those classes in insulation. Thus, the process of embedding such a dataset, which contains composite images, into a continuous space is said to be similar to hierarchy embedding.

There are also some tasks where generic images are used. These images could be related to obscure images because they lack much information. For example, if face recognition software is run over an image that contains a blurry face, the software could match the unclear image with the high-resolution images of many different people.

There are several inherent hierarchies in NLP that go beyond to reach the visual region. For instance, you can use hierarchical grouping to visually represent different species of plants.

Collectively, using hierarchical relations in AI increases the demand for hyperbolic spaces for output embedding. As the volume of Euclidean spaces expands, the resulting expansion is polynomial in nature. However, the expansion of hyperbolic spaces is exponential. This results in the generation of continuous tree analogues.

This information makes it possible to conclude that the unrevealed hierarchy of visual information can be captured by the expanding hyperbolic output embedding.

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