Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decision-making process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learning-based framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries and does not rely on labels or user interactions at all. During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative summaries. Since labels are not required, our method can be fully unsupervised. Extensive experiments on two benchmark datasets show that our unsupervised method not only outperforms other state-of-the-art unsupervised methods, but also is comparable to or even superior than most of published supervised approaches.
Video summarization. Research in video summarization has been significantly advanced in recent years, leading to approaches of various characteristics. @cite_25 identified important objects and people in summarizing videos. @cite_0 learned a linear regressor to predict the degree of interestingness of video frames and selected keyframes with the highest interestingness scores. @cite_22 cast video summarization as a subset selection problem and optimized submodular functions with multiple objectives. @cite_23 applied an attention-modeling technique to extracting keyframes of visual saliency. @cite_9 developed a nonparametric approach to transfer structures of known video summaries to new videos with similar topics. Auxiliary resources have also been exploited to aid the summarization process such as web images videos @cite_18 @cite_15 @cite_4 and category information @cite_24 . Most of these non-deep summarization methods processed video frames independently, thus ignoring the inherent sequential patterns. Moreover, non-deep summarization methods usually do not support end-to-end training, which causes extra costs at test time. To address the aforementioned issues, we model video summarization via a deep RNN to capture long-term dependencies in video frames, and propose a reinforcement learning-based framework to train the network end to end.
@cite_18 @cite_4 @cite_22 @cite_9 @cite_0 @cite_24 @cite_23 @cite_15 @cite_25
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Video summarization is a challenging problem in part because knowing which part of a video is important requires prior knowledge about its main topic. We present TVSum, an unsupervised video summarization framework that uses title-based image search results to find visually important shots. We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic. However, because titles are free-formed, unconstrained, and often written ambiguously, images searched using the title can contain noise (images irrelevant to video content) and variance (images of different topics). To deal with this challenge, we developed a novel co-archetypal analysis technique that learns canonical visual concepts shared between video and images, but not in either alone, by finding a joint-factorial representation of two data sets. We introduce a new benchmark dataset, TVSum50, that contains 50 videos and their shot-level importance scores annotated via crowdsourcing. Experimental results on two datasets, SumMe and TVSum50, suggest our approach produces superior quality summaries compared to several recently proposed approaches. We present video co-summarization, a novel perspective to video summarization that exploits visual co-occurrence across multiple videos. Motivated by the observation that important visual concepts tend to appear repeatedly across videos of the same topic, we propose to summarize a video by finding shots that co-occur most frequently across videos collected using a topic keyword. The main technical challenge is dealing with the sparsity of co-occurring patterns, out of hundreds to possibly thousands of irrelevant shots in videos being considered. To deal with this challenge, we developed a Maximal Biclique Finding (MBF) algorithm that is optimized to find sparsely co-occurring patterns, discarding less co-occurring patterns even if they are dominant in one video. Our algorithm is parallelizable with closed-form updates, thus can easily scale up to handle a large number of videos simultaneously. We demonstrate the effectiveness of our approach on motion capture and self-compiled YouTube datasets. Our results suggest that summaries generated by visual co-occurrence tend to match more closely with human generated summaries, when compared to several popular unsupervised techniques. We present a novel method for summarizing raw, casually captured videos. The objective is to create a short summary that still conveys the story. It should thus be both, interesting and representative for the input video. Previous methods often used simplified assumptions and only optimized for one of these goals. Alternatively, they used handdefined objectives that were optimized sequentially by making consecutive hard decisions. This limits their use to a particular setting. Instead, we introduce a new method that (i) uses a supervised approach in order to learn the importance of global characteristics of a summary and (ii) jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good summary. Experiments on two challenging and very diverse datasets demonstrate the effectiveness of our method, where we outperform or match current state-of-the-art. Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. We propose a novel subset selection technique that leverages supervision in the form of human-created summaries to perform automatic keyframe-based video summarization. The main idea is to nonparametrically transfer summary structures from annotated videos to unseen test videos. We show how to extend our method to exploit semantic side information about the video's category genre to guide the transfer process by those training videos semantically consistent with the test input. We also show how to generalize our method to subshot-based summarization, which not only reduces computational costs but also provides more flexible ways of defining visual similarity across subshots spanning several frames. We conduct extensive evaluation on several benchmarks and demonstrate promising results, outperforming existing methods in several settings. This paper proposes a novel approach and a new benchmark for video summarization. Thereby we focus on user videos, which are raw videos containing a set of interesting events. Our method starts by segmenting the video by using a novel “superframe” segmentation, tailored to raw videos. Then, we estimate visual interestingness per superframe using a set of low-, mid- and high-level features. Based on this scoring, we select an optimal subset of superframes to create an informative and interesting summary. The introduced benchmark comes with multiple human created summaries, which were acquired in a controlled psychological experiment. This data paves the way to evaluate summarization methods objectively and to get new insights in video summarization. When evaluating our method, we find that it generates high-quality results, comparable to manual, human-created summaries. Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. As these videos are generally of poor quality, summarization methods designed for well-produced videos do not generalize to them. To address this challenge, we propose to use web-images as a prior to facilitate summarization of user-generated videos. Our main intuition is that people tend to take pictures of objects to capture them in a maximally informative way. Such images could therefore be used as prior information to summarize videos containing a similar set of objects. In this work, we apply our novel insight to develop a summarization algorithm that uses the web-image based prior information in an unsupervised manner. Moreover, to automatically evaluate summarization algorithms on a large scale, we propose a framework that relies on multiple summaries obtained through crowdsourcing. We demonstrate the effectiveness of our evaluation framework by comparing its performance to that of multiple human evaluators. Finally, we present results for our framework tested on hundreds of user-generated videos. We present a video summarization approach for egocentric or “wearable” camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer's day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video — such as the nearness to hands, gaze, and frequency of occurrence — and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance of objects and people that have never been seen previously. Our results with 17 hours of egocentric data show the method's promise relative to existing techniques for saliency and summarization.