Demo Data from EDBT 2026 / reviewers in the wild / expert
Chunhui Hu
dblp:08/5092
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2003
0000-0002-8314-547XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 3Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
3 papers |
Information retrieval · 87% Knowledge graphs · 13% | |
| Computer graphics and multimedia
2 papers |
Multimedia analysis and retrieval · 100% |
Topics — the 11 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
relevance feedback |
0.1 | 2 | 2003 | Joint semantics and feature based image retrieval using relevance feedback · IEEE Trans. Multim. 2003 A unified framework for semantics and feature based relevance feedback in image retrieval systems · ACM Multimedia 2000 |
Information retrieval
image retrieval |
0.0 | 1 | 2003 | Joint semantics and feature based image retrieval using relevance feedback · IEEE Trans. Multim. 2003 |
Information retrieval › image retrieval
semantic image retrieval |
0.0 | 1 | 2003 | Joint semantics and feature based image retrieval using relevance feedback · IEEE Trans. Multim. 2003 |
Knowledge graphs
semantic network |
0.0 | 1 | 2003 | Joint semantics and feature based image retrieval using relevance feedback · IEEE Trans. Multim. 2003 |
Information retrieval
search engines |
0.0 | 1 | 2001 | IFind: A Web Image Search Engine · SIGIR 2001 |
Information retrieval › image retrieval
web image search |
0.0 | 1 | 2001 | IFind: A Web Image Search Engine · SIGIR 2001 |
Information retrieval › relevance feedback
image retrieval relevance feedback |
0.0 | 1 | 2000 | A unified framework for semantics and feature based relevance feedback in image retrieval systems · ACM Multimedia 2000 |
Information retrieval
retrieval models |
0.0 | 1 | 2000 | A unified framework for semantics and feature based relevance feedback in image retrieval systems · ACM Multimedia 2000 |
Multimedia analysis and retrieval › image retrieval
content-based image retrieval |
0.0 | 1 | 2000 | ***Find - a system for semantics and feature based image retrieval over Internet · ACM Multimedia 2000 |
Multimedia analysis and retrieval
image retrieval |
0.0 | 1 | 2000 | ***Find - a system for semantics and feature based image retrieval over Internet · ACM Multimedia 2000 |
Multimedia analysis and retrieval › cross-modal retrieval › image-text retrieval
keyword-based image retrieval |
0.0 | 1 | 2003 | Joint semantics and feature based image retrieval using relevance feedback · IEEE Trans. Multim. 2003 |
Methods — techniques the papers use, named apart from their topics
relevance feedback · 0.1ranking measure · 0.1semantic network · 0.0optimization · 0.0keyword association · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Joint semantics and feature based image retrieval using relevance feedbackabstractRelevance feedback is a powerful technique for image retrieval and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multilevel image content model have been formulated. However, these methods only perform relevance feedback on low-level image features and fail to address the images' semantic content. In this paper, we propose a relevance feedback framework to take advantage of the semantic contents of images in addition to low-level features. By forming a semantic network on top of the keyword association on the images, we are able to accurately deduce and utilize the images' semantic contents for retrieval purposes. We also propose a ranking measure that is suitable for our framework. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections. HongJiang Zhang, Wenyin Liu, Chunhui Hu |
IEEE Trans. Multim. | 4 |
| 2001 | IFind: A Web Image Search EngineabstractNo abstract available. Zheng Chen 0001, Wenyin Liu, Chunhui Hu, Mingjing Li, HongJiang Zhang |
SIGIR | 3 |
| 2000 | A unified framework for semantics and feature based relevance feedback in image retrieval systemsabstractThe relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multi-level image content model have been formulated. However, these methods only perform relevance feedback on the low-level image features and fail to address the images' semantic content. In this paper, we propose a relevance feedback technique, iFind, to take advantage of the semantic contents of the images in addition to the low-level features. By forming a semantic network on top of the keyword association on the images, we are able to accurately deduce and utilize the images' semantic contents for retrieval purposes. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections. Chunhui Hu, Xingquan Zhu 0001, HongJiang Zhang, Qiang Yang 0001 |
ACM Multimedia | 2 |
| 2000 | ***Find - a system for semantics and feature based image retrieval over InternetabstractNo abstract available. HongJiang Zhang, Wenyin Liu, Chunhui Hu |
ACM Multimedia | 3 |