The concise description of algorithms contains hyperlinks to further information (author, algorithms details, BIB entry, WWW external page).
The algorithm features are: f1 = classification (supervised segmentation), f2 = hiearchy result (manual selection), f3 = known number of regions, f4 = [reserved].
Projective non-negative matrix factorization for unsupervised graph clustering
Unsupervised graph clustering and image segmentation algorithm based on non-negative matrix factorization. It considers arbitrarily represented visual signals (in 2D or 3D) and uses a graph embedding approach for image or point cloud segmentation. It extends a Projective Non-negative Matrix Factorization variant to include local spatial relationships over the image graph. By using properly defined region features, this method can be applied for object and image segmentation.
List of uploaded results for 'GRPNMF' algorithm