Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors
Abstract
We present a novel approach for fast object class recognition incorporating contextual information into boosting. The object is represented as a constellation of generalized correlograms that integrate both information of local parts and their spatial relations. Incorporating the spatial relations into our constellation of descriptors, we show that an exhaustive search for the best matching can be avoided. Combining the contextual descriptors with boosting, the system simultaneously learns the information that characterize each part of the object along with their characteristic mutual spatial relations. The proposed framework includes a matching step between homologous parts in the training set, and learning the spatial pattern after matching. In the matching part two approaches are provided: a supervised algorithm and an unsupervised one. Our results are favorably compared against state-of-the-art results.
Cite
Text
Amores et al. "Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.156Markdown
[Amores et al. "Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/amores2005cvpr-fast/) doi:10.1109/CVPR.2005.156BibTeX
@inproceedings{amores2005cvpr-fast,
title = {{Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors}},
author = {Amores, Jaume and Sebe, Nicu and Radeva, Petia},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {769-774},
doi = {10.1109/CVPR.2005.156},
url = {https://mlanthology.org/cvpr/2005/amores2005cvpr-fast/}
}