EDBT 2026 Demo / reviewers in the wild / expert
Krishnan Parasuraman
dblp:89/4004
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, 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.
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 50% Query processing and optimization · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › image retrieval
content-based image retrieval |
0.0 | 1 | 1998 | ZEBRA Image Access System · ICDE 1998 |
Multimedia analysis and retrieval
image retrieval |
0.0 | 1 | 1998 | ZEBRA Image Access System · ICDE 1998 |
Information retrieval › retrieval models › lexical retrieval
bag-of-words retrieval |
0.0 | 1 | 1998 | ZEBRA Image Access System · ICDE 1998 |
Query processing and optimization
multi-attribute query |
0.0 | 1 | 1998 | ZEBRA Image Access System · ICDE 1998 |
Methods — techniques the papers use, named apart from their topics
visual information retrieval engine · 0.0black box metadata extraction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | ZEBRA Image Access SystemabstractThe ZEBRA system, which is part of the VisualHarness platform for managing heterogeneous data, supports three types of access to distributed image repositories: keyword based, attribute based, and image content based. A user can assign different weights (relative importance) to each of the three types, and within the last type of access, to each of the image properties. The image based access component (IBAC) supports access based on computable image properties such as those based on spatial domain, frequency domain or statistical and structural analysis. However, it uses a novel black box approach of utilizing a Visual Information Retrieval (VIR) engine to compute corresponding metadata that is then independently managed in a relational database to provide query processing involving image features and information correlation. That is, one overcomes the difficulties in using the feature vectors that are proprietary to a VTR engine, as one does not require any knowledge of the internal representation or format of the image feature used by a VIR engine. Srilekha Mudumbai, Kshitij Shah, Amit P. Sheth, Krishnan Parasuraman, Clemens Bertram |
ICDE | 4 |