A New Analysis Framework for Relevance Feedback-Driven Similarity Measure Refinement in Content-Based Image Retrieval
Abstract
Many recent content-based image retrieval techniques utilize relevance feedback (RF) from the user to adjust the system response to better meet user expectations. One school of RF-based methods uses a weighted Minkowski distance metric to assess similarity, and adjusts the weights to refine query response. A new method of estimating these weight vectors is presented which outperforms existing methods, particularly for the important case of limited training data. A new objective function is presented for an iterative optimization routine which more closely aligns optimization goals with true system goals. A new analysis framework is presented in the derivation of this technique which is useful for understanding the limitations of many RF methods.
Cite
Text
Jones and Wilkes. "A New Analysis Framework for Relevance Feedback-Driven Similarity Measure Refinement in Content-Based Image Retrieval." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990623Markdown
[Jones and Wilkes. "A New Analysis Framework for Relevance Feedback-Driven Similarity Measure Refinement in Content-Based Image Retrieval." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/jones2001cvpr-new/) doi:10.1109/CVPR.2001.990623BibTeX
@inproceedings{jones2001cvpr-new,
title = {{A New Analysis Framework for Relevance Feedback-Driven Similarity Measure Refinement in Content-Based Image Retrieval}},
author = {Jones, Brett C. and Wilkes, D. Mitchell},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:920-925},
doi = {10.1109/CVPR.2001.990623},
url = {https://mlanthology.org/cvpr/2001/jones2001cvpr-new/}
}