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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic |
information retrieval |
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topic |
information retrieval |
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topic |
information retrieval |
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topic |
information retrieval |
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topic |
information retrieval |
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topic |
information retrieval |
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topic |
information retrieval |
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topic_interest |
information retrieval |
| knowledge media |
has narrower |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
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topic_interest |
information retrieval |
| information retrieval |
type |
Concept |
| information retrieval |
label |
information retrieval |
| information retrieval |
has broader |
knowledge media |
| information retrieval |
Description |
Our research is centred around the theme of Multimedia Information Retrieval, ie,
Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval,
Query Languages and Query Mediation. We are the founding member and administer MMKM
(UK Multimedia Knowledge Management Network). We focus on content-based information
retrieval over a wide range of data spanning form unstructured text and unlabelled
images over spoken documents and music to videos. This encompasses the modelling of
human perception of relevance and similarity, the learning from user actions and the
up-to-date presentation of information. Currently we are building a research version
of an integrated multimedia information retrieval system MIR to be used as a research
prototype. We aim for a system that understands the user's information need and successfully
links it to the appropriate information sources, be it a report or a TV news clip.
This work is guided by the vision that an automated knowledge extraction system ultimately
empowers people making efficient use of information sources without the burden of
filing data into specialised databases. |