Learning a Multi-Size Patch-Based Hybrid Kernel Machine Ensemble for Abnormal Region Detection in Colonoscopic Images
Abstract
When detecting abnormalities in colonoscopic images, the location, shape and size of the abnormal regions in the image are unknown and vary across images. It is difficult to determine the appropriate patch-size for patch-based approach. So multi-size patches are used simultaneously to represent the image regions and an ensemble is constructed in which each classifier handles one patch size. The combination of classifiers trained using multiple-size patches can recognize abnormal regions more effectively than only using single-size patches. The classification of the image patches can be performed using a discriminative binary support vector machine (SVM) or a recognition-based one-class SVM. Integration of the two types of SVMs is expected to further improve abnormal region detection. Experimental results show the good performance of our proposed ensemble.
Cite
Text
Li et al. "Learning a Multi-Size Patch-Based Hybrid Kernel Machine Ensemble for Abnormal Region Detection in Colonoscopic Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.201Markdown
[Li et al. "Learning a Multi-Size Patch-Based Hybrid Kernel Machine Ensemble for Abnormal Region Detection in Colonoscopic Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/li2005cvpr-learning/) doi:10.1109/CVPR.2005.201BibTeX
@inproceedings{li2005cvpr-learning,
title = {{Learning a Multi-Size Patch-Based Hybrid Kernel Machine Ensemble for Abnormal Region Detection in Colonoscopic Images}},
author = {Li, Peng and Chan, Kap Luk and Krishnan, Shankar Muthu},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {670-675},
doi = {10.1109/CVPR.2005.201},
url = {https://mlanthology.org/cvpr/2005/li2005cvpr-learning/}
}