Krishnan Parasuraman

dblp:89/4004 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › image retrieval
content-based image retrieval
0.011998
ZEBRA Image Access System · ICDE 1998
Multimedia analysis and retrieval
image retrieval
0.011998
ZEBRA Image Access System · ICDE 1998
Information retrieval › retrieval models › lexical retrieval
bag-of-words retrieval
0.011998
ZEBRA Image Access System · ICDE 1998
Query processing and optimization
multi-attribute query
0.011998
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
YearPublicationVenuePosition
1998 ZEBRA Image Access System
abstract
The 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
ICDE4