KMi Publications

Tech Reports

6 Tech Reports | Peter Scott


Symmetrical support in FlashMeeting: a naturalistic study of live online peer-to-peer learning via software videoconferencing
Techreport ID: kmi-07-01
Date: 2007
Author(s): Peter Scott, Linda Castaņeda, Kevin Quick, Jon Linney
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Knowledge work in nursing and midwifery: an evaluation through computer mediated communication
Techreport ID: kmi-06-10
Date: 2006
Author(s): Fiona Brooks, Peter Scott
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Heroic failures in disseminating novel e-learning technologies to corporate clients: a case study of interactive webcasting
Techreport ID: kmi-05-01
Date: 2005
Author(s): Peter Scott, Kevin Quick
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Will Technology Enhanced Learning ever deliver 'genuine' innovation?
Date: 2004
Author(s): Peter Scott
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Assisted Electronic Communication in Nursing
Techreport ID: kmi-04-09
Date: 2004
Author(s): Peter Scott, Fiona Brooks, Kevin Quick, Maria Macintyre, Christine Rospopa
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'You got tagged!': the city as a playground
Techreport ID: kmi-04-03
Date: 2004
Author(s): Yanna Vogiazou, Bas Raijmakers, Ben Clayton, Marc Eisenstadt, Erik Geelhoed, Jon Linney, Kevin Quick, Josephine Reid, Peter Scott
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KMi Publications Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Multimedia and Information Systems is...


Multimedia and Information Systems
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 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.

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