Gaussian Mixture Models and Geodesics

In summary, my friend says that he understands the concept of Gaussian mixture models but has trouble implementing the equations numerically. He also has no idea on the "Geodesic" boundary curve.
  • #1
Sudharaka
Gold Member
MHB
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Hi everyone, :)

This is a question that one of my friends sent me. It is kind of open ended and I don't have any clue about the particular area of research he is undertaking. Therefore I am posting the question here with the hope that anybody knowledgeable in this area would be able to help. :)

Question: This question is with regard to the following research paper.

http://research.microsoft.com/pubs/70262/tr-2006-10.pdf

My friend says, "I have understood the concept of Gaussian mixture models but I have trouble implementing the equations numerically. I also have no idea on the "Geodesic" boundary curve. "
 
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  • #2
Sudharaka said:
Hi everyone, :)

This is a question that one of my friends sent me. It is kind of open ended and I don't have any clue about the particular area of research he is undertaking. Therefore I am posting the question here with the hope that anybody knowledgeable in this area would be able to help. :)

Question: This question is with regard to the following research paper.

http://research.microsoft.com/pubs/70262/tr-2006-10.pdf

My friend says, "I have understood the concept of Gaussian mixture models but I have trouble implementing the equations numerically. I also have no idea on the "Geodesic" boundary curve. "

Hey Sudharaka!

I do not understand what the question is. :confused:

Anyway, I can brainstorm a little seeing the type of research involved.
I can suggest taking a look at the so called Hough Transform. That should help to create numerical equations that assist in recognizing specific shapes.
 
  • #3
Hi all,
I am actually the person Sudharaka is talking about (Wave)
Brief background. I'm trying to implement this image segmentation method as a computer program. I have somewhat of a good knowledge in mathematics but not in areas such as Gaussian Mixture Models.

I have two questions.

1. What is a geodesic active contour mentioned in the paper? How does it defer from a normal contour?

2. How can I represent equations such as [15] & [17] (refer paper) in a computer program?

With regard to question 2, I know it's not much of a mathematics question but if anyone has implemented mathematical formulas involving probability distributions, logarithms and partial derivatives in a computer program, can he/she share the basic methodologies of doing so?

Thanks in advance! And thank you Sudharaka for posting this and introducing me to this forum :)

http://research.microsoft.com/pubs/70262/tr-2006-10.pdf
 

FAQ: Gaussian Mixture Models and Geodesics

What is a Gaussian Mixture Model (GMM)?

A Gaussian Mixture Model is a statistical model used for clustering and density estimation. It assumes that the data is generated from a mixture of several Gaussian distributions, hence the term "mixture". It is a powerful tool for identifying hidden patterns and relationships within data.

How does a Gaussian Mixture Model work?

A GMM works by first randomly assigning data points to a certain number of clusters. It then calculates the probability of each data point belonging to each cluster based on the distribution of the data within each cluster. The model then adjusts the cluster centers and repeats the process until the probabilities converge and the clusters no longer change.

What are the advantages of using a Gaussian Mixture Model?

GMMs are flexible and can identify complex patterns and relationships in data. They can handle data with multiple dimensions and can also handle data with missing values. Additionally, GMMs allow for soft clustering, where data points can belong to multiple clusters with different probabilities.

What are geodesics in the context of Gaussian Mixture Models?

In GMMs, geodesics refer to the shortest distance between two points on a manifold, which is a curved space that represents the relationships between the different clusters. Geodesics can be used to measure the similarity between clusters and can help in identifying the most important clusters in a dataset.

How are Gaussian Mixture Models and geodesics used in real-world applications?

Gaussian Mixture Models and geodesics have a wide range of applications, including image and face recognition, speech recognition, and recommendation systems. They are also used in finance for risk management and in biology for identifying genetic patterns. GMMs and geodesics allow for efficient and accurate data analysis, making them valuable tools in various industries.

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