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 lowdimensional trajectories generated by the proposed method correctly represent the semantic information of the data.