Comparing keyframe summaries of egocentric videos: Closest-to-centroid baseline

Evaluation of keyframe video summaries is a notoriously difficult problem. So far, there is no consensus on guidelines, protocols, benchmarks and baseline models. This study contributes in three ways: (1) We propose a new baseline model for creating a keyframe summary, called Closest-to-Centroid, and show that it is a better contestant compared to the two most popular baselines: uniform sampling and choosing the mid-event frame. (2) We also propose a method for matching the visual appearance of keyframes, suitable for comparing summaries of egocentric videos and lifelogging photostreams. (3) We examine 24 image feature spaces (different descriptors) including colour, texture, shape, motion and a feature space extracted by a pre-trained convolutional neural network (CNN). Our results using the four egocentric videos in the UTE database favour low-level shape and colour feature spaces for use with CC.