ML Anthology

Beyond One-to-One Feature Correspondence: The Need for Many-to-Many Matching and Image Abstraction

Sven J. Dickinson
CVPRW 2009 pp. 12
doi:10.1109/CVPRW.2009.5204333 /cvprw/2009/dickinson2009cvprw-beyond/

Abstract

Summary form only given: In this paper briefly review three formulations of the many-to-many matching problem as applied to model acquisition, model indexing, and object recognition. In the first scenario, I will describe the problem of learning a prototypical shape model from a set of exemplars in which the exemplars may not share a single local feature in common. We formulate the problem as a search through the intractable space of feature combinations, or abstractions, to find the "lowest common abstraction" that is derivable from each input exemplar. This abstraction, in turn, defines a many-to-many feature correspondence among the extracted input features.

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Cite

Text

Dickinson. "Beyond One-to-One Feature Correspondence: The Need for Many-to-Many Matching and Image Abstraction." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204333

Markdown

[Dickinson. "Beyond One-to-One Feature Correspondence: The Need for Many-to-Many Matching and Image Abstraction." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/dickinson2009cvprw-beyond/) doi:10.1109/CVPRW.2009.5204333

BibTeX

@inproceedings{dickinson2009cvprw-beyond,
  title     = {{Beyond One-to-One Feature Correspondence: The Need for Many-to-Many Matching and Image Abstraction}},
  author    = {Dickinson, Sven J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2009},
  pages     = {12},
  doi       = {10.1109/CVPRW.2009.5204333},
  url       = {https://mlanthology.org/cvprw/2009/dickinson2009cvprw-beyond/}
}