Dittmar, C.C.DittmarDunker, P.P.DunkerBegau, A.A.BegauNowak, StefanieStefanieNowakGruhne, MatthiasMatthiasGruhne2022-03-112022-03-112009https://publica.fraunhofer.de/handle/publica/36301010.1109/CBMI.2009.32This paper describes a technical solution for automated slideshow generation by extracting a set of high-level features from music, such as beat grid, mood and genre and intelligently combining this set with image high-level features, such as mood, daytime- and scene classification. An advantage of this high-level concept is to enable the user to incorporate his preferences regarding the semantic aspects of music and images. For example, the user might request the system to automatically create a slideshow, which plays soft music and shows pictures with sunsets from the last 10 years of his own photo collection.The high-level feature extraction on both, the audio and the visual information is based on the same underlying machine learning core, which processes different audio- and visual-low- and mid-level features. This paper describes the technical realization and evaluation of the algorithms with suitable test databases.enTonsignalSignalverarbeitungBildklassifikationMusikMerkmalextraktionsverfahrencross-modal analysishigh-level semantics621006conference paper