Retrieval of logically relevant 3D human motions by Adaptive Feature Selection with Graded Relevance Feedback
A system that can retrieve logically relevant 3D captured motions is useful in game and animation production. We presented a robust logical relevance metric based on the relative distances among the joints. Existing methods select a universal subset of features for all kinds of queries which may not well characterize the variations in different queries. To break through this limitation we proposed an Adaptive Feature Selection (AFS) method that abstracts the characteristics of the query by a Linear Regression Model, and different feature subsets can be selected according to the properties of the specific query. With a Graded Relevance Feedback (GRF) algorithm, we refined the feature subset that enhances the retrieval performance according to the graded relevance of the feedback samples. With an ontology that predefines the logical relevance between motion classes in terms of graded relevance, the performance of the proposed AFSGRF algorithm is evaluated and shown to outperform other class-specific feature selection and motion retrieval methods.
adaptive feature selection graded relevance relevance feedback motion retrieval logical similarity 3d human motion capture