Learning from User Feedback in Image Retrieval Systems
Abstract
We formulate the problem of retrieving images from visual databases as a problem of Bayesian inference. This leads to natural and effective solutions for two of the most challenging issues in the design of a retrieval system: providing support for region-based queries without requiring prior image segmentation, and accounting for user-feedback during a retrieval session. We present a new learning algorithm that relies on belief propagation to account for both positive and negative examples of the user's interests.
Cite
Text
Vasconcelos and Lippman. "Learning from User Feedback in Image Retrieval Systems." Neural Information Processing Systems, 1999.Markdown
[Vasconcelos and Lippman. "Learning from User Feedback in Image Retrieval Systems." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/vasconcelos1999neurips-learning/)BibTeX
@inproceedings{vasconcelos1999neurips-learning,
title = {{Learning from User Feedback in Image Retrieval Systems}},
author = {Vasconcelos, Nuno and Lippman, Andrew},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {977-986},
url = {https://mlanthology.org/neurips/1999/vasconcelos1999neurips-learning/}
}