Using High Dimensional Indexes to Support Relevance Feedback Based Interactive Images Retrieval
Summary: Tackles semantic gap in image retrieval via relevance feedback, using high-dimensional indexes to boost precision and recall. Proposes a B+-tree-like index with cluster splitting and iDistance; demo analyzes adaptive distance updates and index efficiency. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Junqi Zhang
- 2. Xiangdong Zhou
- 3. Wei Wang
- 4. Baile Shi
- 5. Jian Pei
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0032169493 |
| 79 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00056242144 |
| 91 | M-tree: An Efficient Access Method for Similarity Search in Metric Spaces | 1997 | VLDB | 0.0005181666 |
| 3,300 | Indexing the Distance: An Efficient Method to KNN Processing | 2001 | VLDB | 7.2516103e-05 |
| 4,666 | Towards Effective Indexing for Very Large Video Sequence Database | 2005 | SIGMOD | 6.0124339e-05 |
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