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The emergence of low-cost high-quality personal wearable cameras combinedwith the increasing storage capacity of video-sharing websites have evoked agrowing interest in first-person videos, since most videos are composed oflong-running unedited streams which are usually tedious and unpleasant towatch. State-of-the-art semantic fast-forward methods currently face thechallenge of providing an adequate balance between smoothness in visual flowand the emphasis on the relevant parts. In this work, we present theMulti-Importance Fast-Forward (MIFF), a fully automatic methodology tofast-forward egocentric videos facing these challenges. The dilemma of definingwhat is the semantic information of a video is addressed by a learning processbased on the preferences of the user. Results show that the proposed methodkeeps over $3$ times more semantic content than the state-of-the-artfast-forward. Finally, we discuss the need of a particular video stabilizationtechnique for fast-forward egocentric videos.

Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects