Spectral Segmentation Explorer
Build an affinity graph over image patches, take eigenvectors of the Laplacian, and cut the image where the graph is weakest.
- spectral clustering
- graph Laplacian
- affinity
- segmentation
Original patch
Spectral segmentation (2 regions)
Fiedler vector
Sign changes indicate the spectral cut boundary.
Smallest eigenvalues of the graph Laplacian
A large gap suggests the natural number of clusters.
Graph weight matrix
σ_s=3 | σ_i=0.15
Graph construction is itself a model
Small σ_s produces local, fragmented cuts; large σ_s produces global, smooth cuts that may merge distinct anatomical regions. Spectral clustering adds spatial contiguity that intensity k-means lacks, but the weight function's parameters are modeling choices that must be validated, not read off the data.