Cargando…
Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
Spectral clustering methods have been shown to be effective for image segmentation. Unfortunately, the presence of image noise as well as textural characteristics can have a significant negative effect on the segmentation performance. To accommodate for image noise and textural characteristics, this...
Autores principales: | Eichel, Justin A., Wong, Alexander, Fieguth, Paul, Clausi, David A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3873262/ https://www.ncbi.nlm.nih.gov/pubmed/24386111 http://dx.doi.org/10.1371/journal.pone.0082722 |
Ejemplares similares
-
A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy
por: Ahmed, Muhammad Jamal, et al.
Publicado: (2021) -
Projected Affinity Values for Nyström Spectral Clustering
por: He, Li, et al.
Publicado: (2018) -
On a two-truths phenomenon in spectral graph clustering
por: Priebe, Carey E., et al.
Publicado: (2019) -
On the Robustness of Graph-Based Clustering to Random Network Alterations
por: Stacey, R. Greg, et al.
Publicado: (2020) -
Spectral clustering and biclustering: learning large graphs and contingency tables
por: Bolla, Marianna
Publicado: (2013)