Image Segmentation: K-means and Color Spaces

Related lectures (30)

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Covers the basics of image acquisition, including optical devices, resolution factors, lens distortions, and sensor technologies.

Compares K-Means and Spectral Clustering algorithms, highlighting their differences and practical applications in clustering student behaviors.

Covers the fundamentals of clustering in unsupervised learning and its practical applications.

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Explores the relationship between colors and condensation in clustering algorithms.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Page 2 of 2