A relevance feedback algorithm based on RankBoost for content-based motion data retrieval (CBMR) is presented and has two characteristics.First

KNN-DTW is employed as the weak ranker for RankBoost ensemble learning.While adapting to variable-length multivariate time series (VLMTS) data

by taking the advantage of the ensemble and efficiency of RankBoost

it can resolve the conflict between the real-time requirement of relevance feedback and the high computational complexity of VLMTS data.Second

minimizing ranking experience loss and generalization loss risk proposed in this paper are used as the learning objective for RankBoost ensemble learning

which can effectively solve the over-fitting problem caused by small-sample training in relevance feedback.Experimental results on CMU action library verify the effectiveness of the proposed algorithm.