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Image Segmentation with Mean Shift Clustering

BeginnerGuided Project

From image segmentation to anomaly detection, Mean Shift Clustering offers a versatile and powerful solution for a wide range of data analysis challenges. It is no ordinary algorithm - it's a dynamic and non-parametric technique that can navigate through complex data terrains, finding density peaks that lead to clusters of diverse shapes and sizes and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets and use it for image segmentation.

4.5 (181 Reviews)

Language

  • English

Topic

  • Data Science

Enrollment Count

  • 1.32K

Skills You Will Learn

  • Data Science, Clustering, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • May 29, 2025
About this Guided Project
In this guided Project, we will explore Mean Shift Clustering, which is a **non-parametric centroid-based clustering** algorithm. Mean Shift Clustering attempts to group data without having first to be trained on the labeled data. Unlike the K-Means Clustering, when using the Mean Shift, we don't need to specify the number of clusters beforehand. Mean Shift Clustering is used in a wide variety of applications, such as image segmentation, academic ranking systems, search engines, medicine, and many others. 

In the first part of this guided project, we will focus on the image segmentation, which is used in many object detection and tracking systems, as it makes it easier to detect the contour of each object. In the second part, we will show how to use the Mean Shift Clustering to classify the survivors rates of the Titanic, the most famous shipwreck in history. Based on the passengers' features (e.g. age, ticket class, fare, etc.) we will classify the passengers into clusters with different survival probabilities. 


Who should participate?

This guided project is designed for data scientists, machine learning practitioners, and enthusiasts eager to explore non-parametric clustering techniques. Participants should have a basic understanding of Python programming fundamentals. No prior experience with Mean Shift Clustering is required, as we will cover the necessary theory and practical implementations.


Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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Contributors

Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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