Optimizing Multi-Feature Queries for Image Databases

By Ulrich Güntzer, Wolf-Tilo Balke, Werner Kießling. Very Large Data Bases Conference, 2000

391 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

IR-Tree: An Efficient Index for Geographic Document Search (2011, methodology): “Here, the threshold algorithm and its variants [7], [10] can”
Evaluating top-k queries over Web-accessible databases (2002, methodology): “[9] presented variations of Fagin’s original FA algorithm [6] for processing queries over multimedia databases.”
RankSQL: query algebra and optimization for relational top-k queries (2005, background): “In middleware settings, various algorithms are proposed for rank aggregation on a set of objects, by merging multiple ranked lists [10, 26, 16, 11], or scheduling random accesses efficiently [2, 4], with the goal of minimizing number of accesses to objects.”
Optimal aggregation algorithms for middleware (2001, background): “We shall present the ‘‘threshold algorithm’’, or TA. This algorithm was discovered independently by (at least) three groups, including Nepal and Ramakrishna [NR99] (who were the first to publish), Gu¨ ntzer et al. [ GBK00 ], and ourselves.4 For more information and”
Foundations of Preferences in Database Systems (2002, methodology): “[10, 7]) can be used to speed up the computation of rankF under the “top-k” semantics.”
Efficient Distributed Skylining for Web Information Systems (2004, methodology): “We can even expect higher factors by tuning the given heuristic to adapt more closely to the data distribution like shown e.g. in [9].”