  In this paper, we present a new method for egocentric video temporal
segmentation based on integrating a statistical mean change detector and
agglomerative clustering(AC) within an energy-minimization framework. Given the
tendency of most AC methods to oversegment video sequences when clustering
their frames, we combine the clustering with a concept drift detection
technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as
a statistical upper bound for the clustering-based video segmentation. We
integrate both techniques in an energy-minimization framework that serves to
disambiguate the decision of both techniques and to complete the segmentation
taking into account the temporal continuity of video frames descriptors. We
present experiments over egocentric sets of more than 13.000 images acquired
with different wearable cameras, showing that our method outperforms
state-of-the-art clustering methods.
