ML Anthology

Mercer Kernels for Object Recognition with Local Features

Siwei Lyu
CVPR 2005 pp. 223-229
doi:10.1109/CVPR.2005.223 /cvpr/2005/lyu2005cvpr-mercer/

Abstract

A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and semilocal constraints between them. Experimental results of SVM classifiers coupled with the proposed kernels are reported on recognition tasks with the COIL-100 database and compared with existing methods. The proposed kernels achieved competitive performance and were robust to changes in object configurations and image degradations.

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Cite

Text

Lyu. "Mercer Kernels for Object Recognition with Local Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.223

Markdown

[Lyu. "Mercer Kernels for Object Recognition with Local Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/lyu2005cvpr-mercer/) doi:10.1109/CVPR.2005.223

BibTeX

@inproceedings{lyu2005cvpr-mercer,
  title     = {{Mercer Kernels for Object Recognition with Local Features}},
  author    = {Lyu, Siwei},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {223-229},
  doi       = {10.1109/CVPR.2005.223},
  url       = {https://mlanthology.org/cvpr/2005/lyu2005cvpr-mercer/}
}