This is about content-based multimedia retrieval, eg, search of still images similar to given ones; finding a news story broadcast over TV by providing visual examples; finding a music piece by humming it etc. Our retrieval approach is not based on manual annotation but on /automated/ processing and feature extraction.
Some of the challenges in this approach are given by (i) the semantic gap between what computers can index and high-level human concepts and (ii) polysemy which is inherent in visual material and the corresponding wide range of information need by the user. We try to overcome these challenges with interactive retrieval methods, eg, the use of relevance feedback and browsing, thus putting the user at centre stage. Behind the scene we deploy learning algorithms that adapt themselves to the user and their information need.
We argue that these methods, when integrated into digital libraries, will not only enhance their searching and browsing capabilities but also give access through unconventional query methods such as sketching, similarity browsing and providing examples of what is relevant. The talk will cover current work of our multimedia information retrieval team at Imperial College London:
Stefan Rueger is a physicist by training and received a PhD from the Computer Science Department of the Technical University Berlin for his contributions to the theory to Neural Networks. In 1997 he joined Imperial College London to research Data and Text Mining. In 1999 he was awarded an Advanced EPSRC Research Fellowship and, in 2000, a Research Lectureship to work in the area of Multimedia Information Retrieval. His team currently encompasses 6 PhD students and research assistants.
Last modified: Friday, 16-Jul-2004 15:37:31 NZST
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