  Thanks to the low operational cost and large storage capacity of smartphones
and wearable devices, people are recording many hours of daily activities,
sport actions and home videos. These videos, also known as egocentric videos,
are generally long-running streams with unedited content, which make them
boring and visually unpalatable, bringing up the challenge to make egocentric
videos more appealing. In this work we propose a novel methodology to compose
the new fast-forward video by selecting frames based on semantic information
extracted from images. The experiments show that our approach outperforms the
state-of-the-art as far as semantic information is concerned and that it is
also able to produce videos that are more pleasant to be watched.
