Relational Features and Adaptive Segmentation

Even though there is a rapidly growing corpus of motion capture data, there still is a lack of efficient motion retrieval systems that allow to identify and extract user-specified motions. Previous retrieval systems often require manually generated textual annotations, which roughly describe the motions in words. Since the manual generation of reliable and descriptive labels is infeasible for large datasets, one needs efficient content-based retrieval methods that only access the raw data itself. In this context, the query-by-example (QBE) paradigm has attracted a large amount of attention: given a query in form of a motion fragment, the task is to automatically retrieve all motion clips from the database containing parts or aspects similar to the query. The crucial point in such an approach is the notion of similarity used to compare the query with the database motions. For the motion scenario, two motions may be regarded as similar if they represent variations of the same action or sequence of actions. These variations may concern the spatial as well as the temporal domain. For example, the two jumps shown in Fig. 11.1 describe the same kind of motion, even though they differ considerably with respect to timing, intensity, and execution style (note, e.g., the arm swing). Similarly, the kicks shown in Fig. 1.1 describe the same kind of motion, even though they differ considerably with respect to direction and height of the kick. In other words, semantically similar motions need not be numerically similar, as is also pointed out in [107].