Motion Rank One Decomposition and its Application on Motion Retrieval

Recently, low rank decomposition has successfully been applied to human motion retrieval.However, the existing method works on the single motion sequence.Therefore, it ignores the motion correlation in the algorithm level.We propose a low rank decomposition method which could work on motion dataset and all motions share the same set of basis, so our method has clustering effect because similar motion tends to select the same basis.Furthermore, we add the sparse constraint and obtain the effective representation for motion data.In order to achieve this, we present a reasonable construction method for motion data and derive the objective function, based on which, we propose our optimal decomposition algorithm and demonstrate its convergence.We compare our method with other different human motion retrieval approaches and discuss how different parameters of our algorithm affect the results.