An Architecture for Mining Resources Complementary to Audio-Visual Streams

Abstract. In this paper we attempt to characterize resources of information complementary to audio-visual (A/V) streams and propose their usage for enriching A/V data with semantic concepts in order to bridge the gap between low-level video detectors and high-level analysis. Our aim is to extract cross-media feature descriptors from semantically enriched and aligned resources so as to detect finer-grained events in video. We introduce an architecture for complementary resource analysis and discuss domain dependency aspects of this approach related to our domain of soccer broadcasts.

• Type: Conference
• Year: 2007
• Where: SAMT
• Authors: Jan Nemrava, Paul Buitelaar, Thierry Declerck, Vojtech Svátek, Josef Petrak, Andreas Cobet, Herwig Zeiner, David A. Sadlier, Noel E. O'Connor, Nikos Simou, Vassilis Tzouvaras
• Keywords: Complementary Resource Analysis, Cross-media Feature Descriptors, Low-level Video Detectors, SAMT 2007