GRUNTS: Graph Representation for UNsupervised Temporal Segmentation

We propose GRUNTS, a feature independent method for temporal segmentation via unsupervised learning. GRUNTS employs graphs, through skeletonization and polygonal approximation, to represent objects in each frame, and graph matching to efficiently compute a Frame Kernel Matrix able to encode the similarities between frames. We report the results of temporal segmentation in the case of human action recognition, obtained by adopting the Aligned Cluster Analysis (ACA), as unsupervised learning strategy. GRUNTS has been tested on three challenging datasets: the Weizmann dataset, the KTH dataset and the MSR Action3D dataset. Experimental results on these datasets demonstrate the effectiveness of GRUNTS for segmenting actions, mainly compared with supervised learning, typically more computationally expensive and not prone to be real time.