DMCA
Learning a Semantic Space From User’s Relevance Feedback for Image Retrieval (2003)
| Venue: | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
| Citations: | 81 - 4 self |
BibTeX
@ARTICLE{He03learninga,
author = {Xiaofei He and Oliver King and Wei-Ying Ma and Mingjing Li and Hong-Jiang Zhang},
title = {Learning a Semantic Space From User’s Relevance Feedback for Image Retrieval},
journal = {IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY},
year = {2003},
volume = {13},
number = {1},
pages = {39--48}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user’s relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed shortand long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
Keyphrases
image retrieval semantic space user relevance feedback large collection long-term learning process traditional approach content-based retrieval spectral method shortand long-term learning framework relevance feedback retrieval performance image retrieval system user relevance feedback semantic space short-term learning process experimental result current method user interaction
