A relevance feedback algorithm for motion data retrieval

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. © 2016, Chinese Institute of Electronics. All right reserved.