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Details of Grant 

EPSRC Reference: EP/J020257/1
Title: FISH: Fast Semantic Nearest Neighbour Search
Principal Investigator: Qiu, Professor G
Other Investigators:
Garibaldi, Professor JM Aickelin, Professor U
Researcher Co-Investigators:
Project Partners:
Department: School of Computer Science
Organisation: University of Nottingham
Scheme: Standard Research
Starts: 01 June 2012 Ends: 30 September 2013 Value (£): 95,625
EPSRC Research Topic Classifications:
Artificial Intelligence Image & Vision Computing
Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Feb 2012 Data Intensive Systems (DaISy) Announced
Summary on Grant Application Form
This proposal addresses Theme 1 of the Data Intensive Systems (DaISy) Call "Extracting meaningful information: Deriving meaning from large, heterogeneous, incomplete, contradictory, noisy and dispersed data sets with many different forms and formats (e.g. text, image and sound files)". The project will research and develop novel fast semantic nearest neighbour search (FISH) data structure, algorithm and software utilities for rapidly discovering semantically similar neighbours of complex data objects and automatically assigning semantic class labels to them. It will use large scale, high dimensional, heterogeneous, incomplete, and noisy internet image labelling datasets as Case Study for technology development and evaluation. The FISH solutions and utilities can be directly applied to automatically label security surveillance videos and defence reconnaissance imageries to extract semantic meanings to facilitate security and defence intelligence gathering and analysis.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Further Information:  
Organisation Website: http://www.nottingham.ac.uk