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Category-specific video summarization
"... Abstract. In large video collections with clusters of typical categories, such as “birthday party ” or “flash-mob”, category-specific video summa-rization can produce higher quality video summaries than unsupervised approaches that are blind to the video category. Given a video from a known category ..."
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Abstract. In large video collections with clusters of typical categories, such as “birthday party ” or “flash-mob”, category-specific video summa-rization can produce higher quality video summaries than unsupervised approaches that are blind to the video category. Given a video from a known category, our approach first efficiently per-forms a temporal segmentation into semantically-consistent segments, delimited not only by shot boundaries but also general change points. Then, equipped with an SVM classifier, our approach assigns importance scores to each segment. The resulting video assembles the sequence of segments with the highest scores. The obtained video summary is there-fore both short and highly informative. Experimental results on videos from the multimedia event detection (MED) dataset of TRECVID’11 show that our approach produces video summaries with higher relevance than the state of the art.
Video Summarization by Learning Submodular Mixtures of Objectives
- IEEE Conf. Comput. Vis. Pattern Recognit
, 2015
"... We present a novel method for summarizing raw, casu-ally captured videos. The objective is to create a short sum-mary 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 opti-mized for o ..."
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Cited by 4 (2 self)
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We present a novel method for summarizing raw, casu-ally captured videos. The objective is to create a short sum-mary 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 opti-mized for one of these goals. Alternatively, they used hand-defined objectives that were optimized sequentially by mak-ing 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 im-portance of global characteristics of a summary and (ii) jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good sum-mary. Experiments on two challenging and very diverse datasets demonstrate the effectiveness of our method, where we outperform or match current state-of-the-art. 1.
Toward abstractive summarization using semantic representations
, 2015
"... Abstract We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is ge ..."
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Abstract We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-totext generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on goldstandard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization
Creating summaries from user videos
- In ECCV
, 2014
"... Abstract. 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 seg-menting the video by using a novel “superframe ” segmentation, tailored to raw videos. Th ..."
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Abstract. 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 seg-menting 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 psycho-logical 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 re-sults, comparable to manual, human-created summaries.
Storyline Representation of Egocentric Videos with an Application to Story-based Search
"... Egocentric videos are a valuable source of information as a daily log of our lives. However, large fraction of ego-centric video content is typically irrelevant and boring to re-watch. It is an agonizing task, for example, to manually search for the moment when your daughter first met Mickey Mouse f ..."
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Egocentric videos are a valuable source of information as a daily log of our lives. However, large fraction of ego-centric video content is typically irrelevant and boring to re-watch. It is an agonizing task, for example, to manually search for the moment when your daughter first met Mickey Mouse from hours-long egocentric videos taken at Disney-land. Although many summarization methods have been successfully proposed to create concise representations of videos, in practice, the value of the subshots to users may change according to their immediate preference/mood; thus summaries with fixed criteria may not fully satisfy users’ various search intents. To address this, we propose a sto-ryline representation that expresses an egocentric video as a set of jointly inferred, through MRF inference, story el-ements comprising of actors, locations, supporting objects and events, depicted on a timeline. We construct such a sto-ryline with very limited annotation data (a list of map loca-tions and weak knowledge of what events may be possible at each location), by bootstrapping the process with data ob-tained through focused Web image and video searches. Our representation promotes story-based search with queries in the form of AND-OR graphs, which span any subset of story elements and their spatio-temporal composition. We show effectiveness of our approach on a set of unconstrained YouTube egocentric videos of visits to Disneyland. 1.
Video Co-summarization: Video Summarization by Visual Co-occurrence
"... 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 s ..."
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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 chal-lenge 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 de-veloped a Maximal Biclique Finding (MBF) algorithm that is optimized to find sparsely co-occurring patterns, discard-ing 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 num-ber of videos simultaneously. We demonstrate the effective-ness of our approach on motion capture and self-compiled YouTube datasets. Our results suggest that summaries gen-erated by visual co-occurrence tend to match more closely with human generated summaries, when compared to sev-eral popular unsupervised techniques. 1.
