Bottom-up & Top-Down Object Detection Using Primal Sketch Features and Graphical Models
Abstract
A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach. © 2006 IEEE.
Cite
Text
Kokkinos et al. "Bottom-up & Top-Down Object Detection Using Primal Sketch Features and Graphical Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.74Markdown
[Kokkinos et al. "Bottom-up & Top-Down Object Detection Using Primal Sketch Features and Graphical Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/kokkinos2006cvpr-bottom/) doi:10.1109/CVPR.2006.74BibTeX
@inproceedings{kokkinos2006cvpr-bottom,
title = {{Bottom-up & Top-Down Object Detection Using Primal Sketch Features and Graphical Models}},
author = {Kokkinos, Iasonas and Maragos, Petros and Yuille, Alan L.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {1893-1900},
doi = {10.1109/CVPR.2006.74},
url = {https://mlanthology.org/cvpr/2006/kokkinos2006cvpr-bottom/}
}