ARCHIVES Content-based Motion Retrieval Using Vector Space Model

Motion retrieval is the problem of retrieving highly relevant motions in a timely man-ner. The principal challenge is to characterize the similarity between two motions effectively, which is tightly related to the gap between the motion data's represen-tation and its semantics. Our approach uses vector space model to measure the similarities among motions, which are made discrete using the vocabulary technique and transformation invariant using the relational feature model. In our approach, relational features are first extracted from motion data. then such features are clus-tered into a motion vocabulary. Finally motions are turned into bag of words and retrieved using vector-space model. We implemented this new system and tested it on two benchmark databases composed of real world data. Two existing methods, the dynamics time warping method and the binary feature method, are implemented for comparison. The results shows that our system are comparable in effectiveness with the dynamic time warping system, but runs 100 to 400 times faster. In comparison to retrieval with binary features, it is just as fast but more accurate and practical