  Temporal segmentation of untrimmed videos and photo-streams is currently an
active area of research in computer vision and image processing. This paper
proposes a new approach to improve the temporal segmentation of photo-streams.
The method consists in enhancing image representations by encoding long-range
temporal dependencies. Our key contribution is to take advantage of the
temporal stationarity assumption of photostreams for modeling each frame by its
nonlocal self-similarity function. The proposed approach is put to test on the
EDUB-Seg dataset, a standard benchmark for egocentric photostream temporal
segmentation. Starting from seven different (CNN based) image features, the
method yields consistent improvements in event segmentation quality, leading to
an average increase of F-measure of 3.71% with respect to the state of the art.
