We present a new approach to matching graphs embedded in R2 or R3 . Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not req...
Eduard Serradell, Przemyslaw Glowacki, Jan Kybic, ...
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Many existing spectral clustering algorithms share a conventional graph partitioning criterion: normalized cuts (NC). However, one problem with NC is that it poorly captures the g...
We present a unified framework for modeling and solving invariant point pattern matching problems. Invariant features are encoded as potentials in a probabilistic graphical model....
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (Isomap) is one of the representative techniques of man...
Jie Chen, Ruiping Wang, Shiguang Shan, Wen Gao, Xi...