Region-Based Image Annotation Using Asymmetrical Support Vector Machine-Based Multiple-Instance Learning
Abstract
In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.
Cite
Text
Yang et al. "Region-Based Image Annotation Using Asymmetrical Support Vector Machine-Based Multiple-Instance Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.250Markdown
[Yang et al. "Region-Based Image Annotation Using Asymmetrical Support Vector Machine-Based Multiple-Instance Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/yang2006cvpr-region/) doi:10.1109/CVPR.2006.250BibTeX
@inproceedings{yang2006cvpr-region,
title = {{Region-Based Image Annotation Using Asymmetrical Support Vector Machine-Based Multiple-Instance Learning}},
author = {Yang, Changbo and Dong, Ming and Hua, Jing},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {2057-2063},
doi = {10.1109/CVPR.2006.250},
url = {https://mlanthology.org/cvpr/2006/yang2006cvpr-region/}
}