A Hashing Learning Based Motion Capture Data Encoding and Retrieval

In this paper, hashing learning is applied for proposing a method of high efficient motion capture data encoding and content-based retrieval. In the preprocessing step, for each clip sequence of motion capture data in the database, we first extract feature from a number of consecutive frames, called sliding window, to avoid local sensitive of the motion actions, then combing all the sliding window features to form the feature of the motion clip. For a given motion clip, we use a hierarchical strategy to do retrieval. First we hashing encode the representative frames of the clip, then search the entire database using the hashing code to retrieve the first several similar window-clips. Among the selected clips, we finally output those ones that are with minimum accumulated errors computed frame by frame. It is demonstrated by the experiments that the proposed method has the advantage intiming performance when doing retrieval from a very large scale motion capture database.