ML Anthology

Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors

Sudeep Sarkar, Kim L. Boyer
CVPR 1996 pp. 478-483
doi:10.1109/CVPR.1996.517115 /cvpr/1996/sarkar1996cvpr-quantitative/

Abstract

We propose four measures of image organizational change which can be used to monitor construction activity. The measures are based on the thesis that the progress of construction will see a change in the individual image feature attributes as well as an evolution in the relationships among these features. This change in the relationship is captured by the eigenvalues and eigenvectors of the relation graph embodying the organization among the image features. We demonstrate the ability of the measures to differentiate between no development, the onset of construction, and full development, on the available real test image set.

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Cite

Text

Sarkar and Boyer. "Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996. doi:10.1109/CVPR.1996.517115

Markdown

[Sarkar and Boyer. "Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996.](https://mlanthology.org/cvpr/1996/sarkar1996cvpr-quantitative/) doi:10.1109/CVPR.1996.517115

BibTeX

@inproceedings{sarkar1996cvpr-quantitative,
  title     = {{Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors}},
  author    = {Sarkar, Sudeep and Boyer, Kim L.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1996},
  pages     = {478-483},
  doi       = {10.1109/CVPR.1996.517115},
  url       = {https://mlanthology.org/cvpr/1996/sarkar1996cvpr-quantitative/}
}