HISA: A Query System Bridging The Semantic Gap For Large Image Databases
Summary: HISA introduces a data structure unifying ontological semantics with low-level visual features for large-scale image retrieval. Precomputed annotation, ontology/statistical analysis, and two-phase queries support keyword, example-image, and hybrid search. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Gang Chen (Zhejiang University)
- 2. Xiaoyan Li (Zhejiang University)
- 3. Lidan Shou (Zhejiang University)
- 4. Jinxiang Dong (Zhejiang University)
- 5. Chun Chen (Zhejiang University)
BibTeX Citation
@article{chen_vldb06,
title = {{HISA: A Query System Bridging The Semantic Gap For Large Image Databases}},
author = {Chen, Gang and Li, Xiaoyan and Shou, Lidan and Dong, Jinxiang and Chen, Chun},
journal = {PVLDB},
series = {{VLDB} '06},
pages = {1187},
year = {2006}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 46 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00044853085 |
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