People
Members, Coordination, Postdoctoral Fellows, Research Developer, Internal Research Team, Previous Members, Visiting Researchers
Previous Members at KMi
Previous Members at Imperial College
Dr Simon Overell
Simon was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
His research augmented the Geographic Information Retrieval process with information extracted from world knowledge. He approached this from three directions: classifying world knowledge, disambiguating placenames and modelling users. Simon finished his PhD thesis Geographic Information Retrieval: Classification, Disambiguation and Modelling, in 2009 and has since joined TrueKnowledge Ltd in Cambridge.
Simon was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
His research augmented the Geographic Information Retrieval process with information extracted from world knowledge. He approached this from three directions: classifying world knowledge, disambiguating placenames and modelling users. Simon finished his PhD thesis Geographic Information Retrieval: Classification, Disambiguation and Modelling, in 2009 and has since joined TrueKnowledge Ltd in Cambridge.
Dr João Magalhães
João was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
His PhD thesis Statistical models for semantic-multimedia information retrieval, published in 2008, has improved multimedia search by exploring semantic-multimedia analysis through two complementary search paradigms: (1) search-by-keyword and (2) search-by-semantic-example. After his PhD studies João joined Instituto Superior de Engenharia de Lisboa (ISEL), Portugal, as Adjunct Professor.
João was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
His PhD thesis Statistical models for semantic-multimedia information retrieval, published in 2008, has improved multimedia search by exploring semantic-multimedia analysis through two complementary search paradigms: (1) search-by-keyword and (2) search-by-semantic-example. After his PhD studies João joined Instituto Superior de Engenharia de Lisboa (ISEL), Portugal, as Adjunct Professor.
Dr Alexei Yavlinsky
Alexei was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
The title of his PhD thesis was Image indexing and retrieval using automated annotation, published in 2007, argues that models of simple image features, such as global colour and texture, can be used to predict instances of different objects and scenes within photographic images. On this basis Alexei proposed the use of nonparametric density estimation to model these features and thus endow unlabelled images with probabilities of containing particular objects and scenes. This process, termed automated image annotation, enables one to set up a scalable image indexing framework that allows users to retrieve unlabelled images from large collections using simple keyword queries.
Alexei was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
The title of his PhD thesis was Image indexing and retrieval using automated annotation, published in 2007, argues that models of simple image features, such as global colour and texture, can be used to predict instances of different objects and scenes within photographic images. On this basis Alexei proposed the use of nonparametric density estimation to model these features and thus endow unlabelled images with probabilities of containing particular objects and scenes. This process, termed automated image annotation, enables one to set up a scalable image indexing framework that allows users to retrieve unlabelled images from large collections using simple keyword queries.
Dr Peter Howarth
Peter was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
His thesis Discovering images: features, similarities and subspaces, published in 2007, investigates three of the core components of content-based image retrieval: visual features, similarity functions and indexing methods. In the content-based paradigm images are searched in a purely visual domain, where they are represented by high-dimensional features. Exhaustively searching this feature space can take a prohibitively long time. The thesis argues that by the use of judicious approximations we can search large collections interactively, while keeping a good level of retrieval performance. Using normal hardware, Peter's system was able to search four million images in just over one second, thus satisfying the goal of effective real-time searching of large image collections.
Peter joined Redington Partners, London, after his PhD research.
Peter was a a full-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger.
His thesis Discovering images: features, similarities and subspaces, published in 2007, investigates three of the core components of content-based image retrieval: visual features, similarity functions and indexing methods. In the content-based paradigm images are searched in a purely visual domain, where they are represented by high-dimensional features. Exhaustively searching this feature space can take a prohibitively long time. The thesis argues that by the use of judicious approximations we can search large collections interactively, while keeping a good level of retrieval performance. Using normal hardware, Peter's system was able to search four million images in just over one second, thus satisfying the goal of effective real-time searching of large image collections.
Peter joined Redington Partners, London, after his PhD research.
Ed Schofield
Ed was a a part-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger. His thesis Fitting maximum entropy models on large sample spaces, published in 2006, investigates the iterative application of Monte Carlo methods to the problem of parameter estimation for models of maximum entropy, minimum divergence, and maximum likelihood among the class of exponential-family densities. It describes a suite of tools for applying such models to large domains in which exact computation is not practically possible. After his PhD Ed held a postdoctoral position at Cancer Research, UK, and later joined Immense Ltd.
Ed was a a part-time PhD student in information retrieval at the Department of Computing, Imperial College London, in the Multimedia and Information Systems Group, supervised by Prof Stefan Rüger. His thesis Fitting maximum entropy models on large sample spaces, published in 2006, investigates the iterative application of Monte Carlo methods to the problem of parameter estimation for models of maximum entropy, minimum divergence, and maximum likelihood among the class of exponential-family densities. It describes a suite of tools for applying such models to large domains in which exact computation is not practically possible. After his PhD Ed held a postdoctoral position at Cancer Research, UK, and later joined Immense Ltd.
Dr Paul Browne
2005 - 2006
Postdoctoral Research Associate at Imperial College London, Multimedia Digital Libraries
2005 - 2006
Postdoctoral Research Associate at Imperial College London, Multimedia Digital Libraries
Dr Daniel Heesch
PhD student at Imperial College London
PhD thesis (2001 - 2005): "The NN k technique for image searching and browsing."
Also was Research Associate, CHLT EU-NSF project, information visualisation.
PhD student at Imperial College London
PhD thesis (2001 - 2005): "The NN k technique for image searching and browsing."
Also was Research Associate, CHLT EU-NSF project, information visualisation.
Dr Shyamala Doraisamy
PhD student at Imperial College London
PhD thesis (2000 - 2004): "Polyphonic Music Retrieval".
Now lecturer at University Putra Malaysia
PhD student at Imperial College London
PhD thesis (2000 - 2004): "Polyphonic Music Retrieval".
Now lecturer at University Putra Malaysia
Dr Marcus Pickering
PhD student at Imperial College London
PhD thesis (2000 - 2004): "Video Retrieval and Summarisation"
PhD student at Imperial College London
PhD thesis (2000 - 2004): "Video Retrieval and Summarisation"
Alex May
PhD student at Imperial College London
PhD thesis (MEng 2004): "Wed-based video and image navigation"
PhD student at Imperial College London
PhD thesis (MEng 2004): "Wed-based video and image navigation"
Matthew Carey
PhD student at Imperial College London
PhD thesis (MSC 2000): "Information Visualisation"
PhD student at Imperial College London
PhD thesis (MSC 2000): "Information Visualisation"
