||Modern information society is experiencing an explosion of digital content, comprising text, audio, video and graphics. The challenge is to organize, understand, and search multimodal information in a robust, efficient and intelligent manner. One challenge arises from the fact that multimedia objects, even though they are similar from a structural or semantic viewpoint, often reveal significant spatial or temporal differences. This makes content-based multimedia retrieval a challenging research field with many unsolved problems.
In my habilitation project conducted at Bonn University, we studied fundamental algorithms and concepts for the analysis, classification, indexing, and retrieval of time-dependent data streams by means of two different types of multimedia data: waveform-based music data and human motion data. In the music domain, we developed techniques for automatic music alignment, synchronization, and matching. The common goal of these tasks is to automatically link several types of music representations, thus coordinating the multiple information sources related to a given musical work. In the motion domain, we introduced a general and unified framework for motion analysis, retrieval, and classification using binary features to represent poses. By handling spatio-temporal motion deformations already on the feature level, we were able to adopt efficient indexing methods allowing for flexible and efficient content-based retrieval for large motion capture data sets.