title: Robust spectral clustering using statistical sub-graph affinity model.
abstract: 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 study introduces the concept of sub-graph affinity, where each node in the primary graph is modeled as a sub-graph characterizing the neighborhood surrounding the node. The statistical sub-graph affinity matrix is then constructed based on the statistical relationships between sub-graphs of connected nodes in the primary graph, thus counteracting the uncertainty associated with the image noise and textural characteristics by utilizing more information than traditional spectral clustering methods. Experiments using both synthetic and natural images under various levels of noise contamination demonstrate that the proposed approach can achieve improved segmentation performance when compared to existing spectral clustering methods.
authors.affiliation: Department of Systems Design Engineering, U. of Waterloo, Waterloo, Canada.
authors.forename: Justin A
authors.initials: JA
authors.lastname: Eichel
authors.affiliation: Department of Systems Design Engineering, U. of Waterloo, Waterloo, Canada.
authors.forename: Alexander
authors.initials: A
authors.lastname: Wong
authors.affiliation: Department of Systems Design Engineering, U. of Waterloo, Waterloo, Canada.
authors.forename: Paul
authors.initials: P
authors.lastname: Fieguth
authors.affiliation: Department of Systems Design Engineering, U. of Waterloo, Waterloo, Canada.
authors.forename: David A
authors.initials: DA
authors.lastname: Clausi
mesh_terms: D016477:Artifacts; D016000:Cluster Analysis; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D012984:Software