Media understanding is the science/art of identifying semantic structures in digital media objects such as audio, biosignals, images, text and videos. Computational media understanding should do what our senses and cognition do: immediate understanding of events as diverse as watching a bird and listening to a speech. This book introduces the reader with the state-of-the-art methods applied today for media summarization and for the categorization of events. In contrast to related publications, it does t focus on one type of media but considers all the above-named as well as a few others. The author endeavors to identify similarities between the methods employed in audio retrieval, image understanding, text summarization and many other research domains. It turns out that a number of significant parallels do exist. Structuring the methods along common criteria and discussing their similarities and differences breaks the ground for a new research discipline: true computational understanding of multimedia content.
Horst Eidenberger is associate professor of applied computer science at the Vienna University of Technology. He received his Doctor degree in 2000 from the University of Vienna and finished his Habilitation in 2005. He has published several books and more than 70 scientific papers in journals and at international conferences. His research interests include automated content understanding, machine learning and signal processing.