Multiple video trajectories representation using double-layer isometric feature mapping

This paper proposes a novel non-linear dimensionality reduction algorithm, named double-layer isometric feature mapping (DLIso), which generates the trajectories for the video sequence containing different kinds of video clips. First, a nearest neighbor based clustering algorithm is utilized to partition the video sequence into a set of data blocks. Second, intra-cluster graphs are constructed based on the individual character of each data block to build the basic layer for DLIso. Third, the inter-cluster graph is constructed by analyzing the interrelation among these isolated data blocks to build the hyper-layer. Finally, all data points are mapped onto a unique low-dimensional feature space while preserving the corresponding relations in the double layers. Experiments on synthetic datasets as well as the real video sequences demonstrate that the low-dimensional trajectories generated by the proposed method correctly represent the semantic information of the data.