Automatically Creating Adaptive Video Summaries Using Constraint Satisfaction Programming: Application to Sport Content

Boukadida, Haykel; Berrani, Sid-Ahmed; Gros, Patrick

doi:10.1109/tcsvt.2015.2513678

Cited by 16 publications

(5 citation statements)

References 34 publications

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“…On the one hand, the attention curve is usually constructed with one or several low-level features. However, one feature cannot well reflect the users attention, and several features cannot typically guarantee a correlation with what the user is interested in [35]. On the other hand, due to the unsupervised characteristics, the existing attention-based approaches cannot take advantage of human guidance.…”

Section: Attention-based Video Summarization Approachesmentioning

confidence: 99%

Video Summarization With Attention-Based Encoder–Decoder Networks

Xiong

Pang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

285

180

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This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named Attentive encoder-decoder networks for Video Summarization (AVS), in which the encoder uses a Bidirectional Long Short-Term Memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on two video summarization benchmark datasets, i.e., SumMe, and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches, with remarkable improvements from 0.8% to 3% on two datasets, respectively.

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Section: Attention-based Video Summarization Approachesmentioning

confidence: 99%

Video Summarization With Attention-Based Encoder–Decoder Networks

Xiong

Pang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

285

180

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show abstract

“…Video can be summarized by constraints [48,4,6] or presented as a storyboard [23]. Editing tools can be tailored for specific domains, such as conversational video [40,22], interviews [5], physical tasks [14,57] for particular types of footage [25,58].…”

Section: Computational Video Editingmentioning

confidence: 99%

“…We formulate temporal planning of video creation to be an optimization problem. Prior work has demonstrated methods to optimize video editing from raw footage [48,6]. In our domain, given temporal constraints and a list of evaluated assets, URL2Video optimizes the overall score of visual importance.…”

Section: Constraint-based Temporal Planningmentioning

confidence: 99%

Automatic Video Creation From a Web Page

Chi

Sun

Panovich³

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Creating marketing videos from scratch can be challenging, especially when designing for multiple platforms with different viewing criteria. We present URL2Video, an automatic approach that converts a web page into a short video given temporal and visual constraints. URL2Video captures quality materials and design styles extracted from a web page, including fonts, colors, and layouts. Using constraint programming, URL2Video's design engine organizes the visual assets into a sequence of shots and renders to a video with user-specified aspect ratio and duration. Creators can review the video composition, modify constraints, and generate video variation through a user interface. We learned the design process from designers and compared our automatically generated results with their creation through interviews and an online survey. The evaluation shows that URL2Video effectively extracted design elements from a web page and supported designers by bootstrapping the video creation process.

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“…Constraint Satisfaction Programming (CSP) is used in [13] to specify a set of conditions for summary generation. The authors suggest a clear advantage of summary generation rules (specifying conditions) over summary generation algorithms, as new constraints can be added/removed at any time without any alteration.…”

Section: Mono-view/ Single Videomentioning

confidence: 99%

“…In [13,18,48,83] and [178] perform subjective evaluation along with objective evaluation using datasets. [83] uses the subjective criteria of pleasantness and visual informativeness to evaluate multi-view summary.…”

Section: Both Subjective and Objective Evaluationmentioning

confidence: 99%

Video Skimming: Taxonomy and Comprehensive Survey

K.,

Sen,

Raman

2019

Preprint

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Video skimming, also known as dynamic video summarization, generates a temporally abridged version of a given video. Skimming can be achieved by identifying significant components either in uni-modal or multimodal features extracted from the video. Being dynamic in nature, video skimming, through temporal connectivity, allows better understanding of the video from its summary. Having this obvious advantage, recently, video skimming has drawn the focus of many researchers benefiting from the easy availability of the required computing resources. In this paper, we provide a comprehensive survey on video skimming focusing on the substantial amount of literature from the past decade. We present a taxonomy of video skimming approaches, and discuss their evolution highlighting key advances. We also provide a study on the components required for the evaluation of a video skimming performance.

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Automatically Creating Adaptive Video Summaries Using Constraint Satisfaction Programming: Application to Sport Content

Cited by 16 publications

References 34 publications

Video Summarization With Attention-Based Encoder–Decoder Networks

Video Summarization With Attention-Based Encoder–Decoder Networks

Automatic Video Creation From a Web Page

Video Skimming: Taxonomy and Comprehensive Survey

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