ML Anthology

Information Theoretic Clustering of Large Structural Modelbases

Kuntal Sengupta, Kim L. Boyer
CVPR 1993 pp. 174-179
doi:10.1109/CVPR.1993.340992 /cvpr/1993/sengupta1993cvpr-information/

Abstract

A hierarchically structured approach to organizing large structural model bases using an information theoretic criterion is presented. Objects (patterns) are modeled in the form of random parametric structural descriptions (RPSDs), an extension of the parametric structural description graph-theoretic formalism. Hierarchically clustering the RPSDs reduces the computational work to O(log N). The node pointers allow a mapping between the observation and a stored representation at one level, and the mapping to all potential models at all subsequent levels is reduced to mere tests, eliminating the exponential search for the best interprimitive mapping function for each stored candidate pattern.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Sengupta and Boyer. "Information Theoretic Clustering of Large Structural Modelbases." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.340992

Markdown

[Sengupta and Boyer. "Information Theoretic Clustering of Large Structural Modelbases." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/sengupta1993cvpr-information/) doi:10.1109/CVPR.1993.340992

BibTeX

@inproceedings{sengupta1993cvpr-information,
  title     = {{Information Theoretic Clustering of Large Structural Modelbases}},
  author    = {Sengupta, Kuntal and Boyer, Kim L.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1993},
  pages     = {174-179},
  doi       = {10.1109/CVPR.1993.340992},
  url       = {https://mlanthology.org/cvpr/1993/sengupta1993cvpr-information/}
}