Visual Mining - Interpreting Image and Video

Thursday 14 April 2016, 6.00pm - 8.00pm

The Davidson Building, 5 Southampton Street, London WC2E 7HA. The nearest underground stations are Covent Garden and Charing Cross.

Free, but pre-booking is required.

Professor Stefan Rüger, The Open University


Like text mining, visual media mining tries to make sense of the world through algorithms - albeit by analysing pixels instead of words.

This talk highlights recent important technical advances in automated media understanding, which has a variety of applications ranging from machines emulating the human aesthetic judgment of photographs to typical visual mining tasks such as analysing food images. Highlighted techniques include near-duplicate detection, multimedia indexing and the role of machine learning. While the first two enable visual search engines so that, eg, a snapshot of a smartphone alone links the real world to databases with information about it, machine learning ultimately is the key to endowing machines with human capabilities of recognition and interpretation. The talk will end by looking into the crystal ball exploring what machines might learn from automatically analysing tens of thousands of hours of TV footage.

About the speaker:

Prof Stefan Rüger read Physics at Freie Universität Berlin and gained his PhD in Computer Science at Technische Universität Berlin (1996) on the Theory of Neural Networks. During the next decade he carved out his academic career from postdoc to Reader researching multimedia information retrieval at Imperial College London where he also held an EPSRC Advanced Research Fellowship (1999-2004). In 2006 Stefan became a Professor of Knowledge Media when he joined The Open University's Knowledge Media Institute to cover the area of Multimedia and Information Systems. He currently holds an Honorary Professorship from the University of Waikato, New Zealand, and has held Visiting Fellowships at Imperial College London and Cranfield University, UK. Stefan is interested in the intellectual challenge of visual processing with a view to automated multimedia understanding.


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