Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding

Schweitzer, Haim

doi:10.1109/ICCV.2001.937676

Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding

Haim Schweitzer

ICCV 2001 pp. 566-571

doi:10.1109/ICCV.2001.937676 /iccv/2001/schweitzer2001iccv-template/

Abstract

Content based indexing is computed from input that consists of matching values between images and templates. The key idea is to embed both images and templates in a low-dimensional Euclidean space so that matching between embedded images and embedded templates approximates the given input. It is shown that such embedding can be computed by means of a singular value decomposition of the input matrix. Classic principal component analysis is shown to be a special case of the proposed technique, corresponding to the case where the templates and the images are the same.

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Text

Schweitzer. "Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding." IEEE/CVF International Conference on Computer Vision, 2001. doi:10.1109/ICCV.2001.937676

Markdown

[Schweitzer. "Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding." IEEE/CVF International Conference on Computer Vision, 2001.](https://mlanthology.org/iccv/2001/schweitzer2001iccv-template/) doi:10.1109/ICCV.2001.937676

BibTeX

@inproceedings{schweitzer2001iccv-template,
  title     = {{Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding}},
  author    = {Schweitzer, Haim},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2001},
  pages     = {566-571},
  doi       = {10.1109/ICCV.2001.937676},
  url       = {https://mlanthology.org/iccv/2001/schweitzer2001iccv-template/}
}