Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback
Abstract
A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two parts. Images on the positive side of the boundary are ranked by their Euclidean distances to the query. The scheme is called restricted similarity measure (RSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance based on the Euclidean distance measure. Two techniques, support vector machine and AdaBoost, are utilized to learn the boundary, and compared with respect to their performance in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The RSM metric is evaluated on a large database of 10,009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.
Cite
Text
Guo et al. "Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990548Markdown
[Guo et al. "Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/guo2001cvpr-learning/) doi:10.1109/CVPR.2001.990548BibTeX
@inproceedings{guo2001cvpr-learning,
title = {{Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback}},
author = {Guo, Guodong and Jain, Anil K. and Ma, Wei-Ying and Zhang, HongJiang},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:731-736},
doi = {10.1109/CVPR.2001.990548},
url = {https://mlanthology.org/cvpr/2001/guo2001cvpr-learning/}
}