3D human motion retrieval using graph kernels based on adaptive graph construction

Graphs are frequently used to provide a powerful representation for structured data. However, it is still a challenging task to model 3D human motions due to its large spatio-temporal variations. This paper proposes a novel graph-based method for real time 3D human motion retrieval. Firstly, we propose a novel graph construction method which connects the joints that are deemed important for a given motion. In particular, the top-N Relative Ranges of Joint Relative Distances (RRJRD) were proposed to determine which joints should be connected in the resulting graph because these measures indicate the normalized activity levels among the joint pairs. Different motions may thus result in different graph structures so the construction of the graphs is made adaptive to the characteristics of a given motion and is able to represent a meaningful spatial structure. In addition to the spatial structure, the temporal pyramid of covariance descriptors was adopted to preserve certain level of spatio-temporal local features. The graph kernel is computed by matching the walks from each of the two graphs to be matched. Furthermore, multiple kernel learning was applied to determine the optimal weights for combining the graph kernels to measure the overall similarity between two motions. The experimental results show that our method is robust under several variations, and demonstrates superior performance in comparison to three state-of-the-art methods. Graphical abstractDisplay Omitted HighlightsWe propose a novel adaptive graph model for representation of 3D human motions.We propose an AGK to measure the similarity of two adaptive graphs.We propose a method which allows AGK set weights to the different matched walk groups.