Title
Hilbert Functions and Applications to the Estimation of Subspace Arrangements
Abstract
This paper develops a new mathematical framework for studying the subspace-segmentation problem. We examine some important algebraic properties of subspace arrangements that are closely related to the subspace-segmentation problem. More specifically, we introduce an important class of invariants given by the Hilbert functions. We show that there exist rich relations between subspace arrangements and their corresponding Hilbert functions. We propose a new subspace-segmentation algorithm, and showcase two applications to demonstrate how the new theoretical revelation may solve subspace segmentation and model selection problems under less restrictive conditions with improved results.
Year
DOI
Venue
2005
10.1109/ICCV.2005.114
ICCV
Keywords
Field
DocType
Hilbert spaces,image segmentation,Hilbert function,algebraic property,subspace arrangement estimation,subspace-segmentation problem
Scale-space segmentation,Computer science,Segmentation-based object categorization,Hilbert series and Hilbert polynomial,Hilbert R-tree,Artificial intelligence,Hilbert space,Mathematical optimization,Algebra,Pattern recognition,Subspace topology,Model selection,Invariant (mathematics)
Conference
Volume
ISSN
ISBN
1
1550-5499
0-7695-2334-X-01
Citations 
PageRank 
References 
5
0.76
8
Authors
5
Name
Order
Citations
PageRank
Allen Y. Yang15216183.98
shankar rao250.76
Aaron B. Wagner332237.39
Yi Ma414931536.21
r m fossum51156.19