Content-based retrieval for human motion data

In this study, we propose a novel framework for constructing a content-based human motion retrieval system. Two major components, including indexing and matching, are discussed and their corresponding algorithms are presented. In indexing, we introduce an affine invariant posture feature and propose an index map structure based on the posture distribution of raw data. To avoid the curse of dimensionality, the high-dimension posture feature of the entire skeleton is decomposed into the direct sum of low-dimension segment-posture features of skeletal segments. In matching, the start and end frames of a query example are first indexed into index maps to find candidate clips from the given motion collection. Then the similarity between the query example and each candidate clip is computed through dynamic time warping. Some experimental examples are given to demonstrate the effectiveness and efficiency of proposed algorithms. (C) 2004 Elsevier Inc. All rights reserved.