Content-Based Image Retrieval Using Multiple-Instance Learning
Abstract
We explore the application of machine learning techniques to the problem of content-based image retrieval (CBIR). Unlike most existing CBIR systems in which only global information is used or in which a user must explicitly indicate what part of the image is of interest, we apply the multiple-instance (MI) learning model to use a small number of training images to learn what images from the database are of interest to the user.
Cite
Text
Zhang et al. "Content-Based Image Retrieval Using Multiple-Instance Learning." International Conference on Machine Learning, 2002.Markdown
[Zhang et al. "Content-Based Image Retrieval Using Multiple-Instance Learning." International Conference on Machine Learning, 2002.](https://mlanthology.org/icml/2002/zhang2002icml-content/)BibTeX
@inproceedings{zhang2002icml-content,
title = {{Content-Based Image Retrieval Using Multiple-Instance Learning}},
author = {Zhang, Qi and Goldman, Sally A. and Yu, Wei and Fritts, Jason E.},
booktitle = {International Conference on Machine Learning},
year = {2002},
pages = {682-689},
url = {https://mlanthology.org/icml/2002/zhang2002icml-content/}
}