With the growing popularity of short-form video sharing platforms such as Instagram and Vine , there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube. Based on the idea that the most significant sub-events within a video class are commonly present among edited videos while less interesting ones appear less frequently, we identify the significant sub-events via a robust recurrent auto-encoder trained on a collection of user-edited videos queried for each particular class of interest. The auto-encoder is trained using a proposed shrinking exponential loss function that makes it robust to noise in the web-crawled training data, and is configured with bidirectional long short term memory (LSTM) LSTM:97 cells to better model the temporal structure of highlight segments. Different from supervised techniques, our method can infer highlights using only a set of downloaded edited videos, without also needing their pre-edited counterparts which are rarely available online. Extensive experiments indicate the promise of our proposed solution in this challenging unsupervised settin
@cite_1: We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a search query (domain) such as “surfing”, our system mines the Youtube database to find pairs of raw and corresponding edited videos. Leveraging the assumption that edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to robustly deal with the noise. We efficiently learn the latent model on a large number of videos (about 700 minutes in all) using a novel EM-like self-paced model selection procedure. Our latent ranking model outperforms its classification counterpart, a motion analysis baseline [15], and a fully-supervised ranking system that requires labels from Amazon Mechanical Turk. Finally, we show that impressive highlights can be retrieved without additional human supervision for domains like skating, surfing, skiing, gymnastics, parkour, and dog activity in unconstrained personal videos.
As defined in @cite_1 , a video highlight is a moment of major or special interest in a video. Generating highlight clips is thus different from the task of video summarization, which instead accounts for factors such as diversity'' and representativeness'' to convey a brief but comprehensive synopsis of a video. Despite this different goal, we review methods for video summarization in addition to video highlight detection because of similarities between the two topics.