An Effective Video Summarization Framework Toward Handheld Devices

Zhang, Luming; Xia, Yingjie; Mao, Kuang; Ma, He; Shan, Zhenyu

doi:10.1109/tie.2014.2336639

Cited by 73 publications

(16 citation statements)

References 43 publications

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“…Such a summary can be a great source of information for prospective tourists when they plan to visit the place and would like to get a preview of its main parts 4 . It is also important to note that all prior works [78], [77], [42], [41] conducted experiments on personal test sets, which are not publicly available, thus making it hard for others to reproduce or to compare the presented results. We hope the release of our Tour20 dataset will give researchers a new, dynamic tool to evaluate their video summarization algorithms in a repeatable and efficient way 5 .…”

Section: A Topic-oriented Video Summarizationmentioning

confidence: 99%

Diversity-Aware Multi-Video Summarization

Panda

Mithun

Roy-Chowdhury

2017

IEEE Trans. on Image Process.

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Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos. We observe that each video in the collection may contain some information that other videos do not have, and thus exploring the underlying complementarity could be beneficial in creating a diverse informative summary. We develop a novel diversity-aware sparse optimization method for multi-video summarization by exploring the complementarity within the videos. Our approach extracts a multi-video summary, which is both interesting and representative in describing the whole video collection. To efficiently solve our optimization problem, we develop an alternating minimization algorithm that minimizes the overall objective function with respect to one video at a time while fixing the other videos. Moreover, we introduce a new benchmark data set, Tour20, that contains 140 videos with multiple manually created summaries, which were acquired in a controlled experiment. Finally, by extensive experiments on the new Tour20 data set and several other multi-view data sets, we show that the proposed approach clearly outperforms the state-of-the-art methods on the two problems-topic-oriented video summarization and multi-view video summarization in a camera network.

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Section: A Topic-oriented Video Summarizationmentioning

confidence: 99%

Diversity-Aware Multi-Video Summarization

Panda

Mithun

Roy-Chowdhury

2017

IEEE Trans. on Image Process.

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“…In [103] the authors used video summarization to provide a mash-up of several videos into a unique pleasant video containing the important sequences of all the concatenated videos. This approach shows the possible extension of the notion of summarization from a single image or video document to a whole archive of documents.…”

Section: Attention-based Summarizationmentioning

confidence: 99%

Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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International audienceThis chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models. There are numerous applications and we try here to remain as exhaustive as possible to provide a picture of all the applications of saliency models, but also to detect where future developments might be of interest. The applications are grouped into three categories. The first one uses the detection of the most important regions in an image and contains applications such as video surveillance, audio surveillance, defect detection, pathology detection, expressive and social gestures, computer graphics and quality metrics. The second category uses saliency maps to detect the regions which are the less interesting in an image. Here one can found applications like texture metrics, compression, retargeting, summarization, watermarking and attention-based ad insertion. Finally, a third category uses the most interesting areas in an image with further processing like comparisons between those areas. In this category one can find image registration and landmarks, object recognition, action guidance in robotics or avatars, web sites optimization, images memorability, best viewpoint, symmetries and automatic focus on images

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“…A multi-video summarization approach is proposed [51] to generate condensed, descriptive, and aesthetically pleasing video summary for multiple sightseeing videos. The authors have applied the multi-task feature selection approach to discover the semantically important features from videos wherein video frames that can efficiently reconstruct the salient objects in the videos are chosen as keyframes.…”

Section: Movie Summarizationmentioning

confidence: 99%

“…The authors have applied the multi-task feature selection approach to discover the semantically important features from videos wherein video frames that can efficiently reconstruct the salient objects in the videos are chosen as keyframes. Also, the authors in [51] used a probabilistic model for 7 fitting the keyframes into aesthetically pleasing, and coherent video summaries. In our proposed work, summary construction is carried out using a constrained optimization approach where shots/scenes that are semantically relevant to the user's interest are selected and temporally ordered for constructing a movie summary.…”

Section: Movie Summarizationmentioning

confidence: 99%

What do you wish to see? A summarization system for movies based on user preferences

Kannan

Ghinea

Swaminathan³

2015

Information Processing & Management

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Abstract. Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.

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An Effective Video Summarization Framework Toward Handheld Devices

Cited by 73 publications

References 43 publications

Diversity-Aware Multi-Video Summarization

Diversity-Aware Multi-Video Summarization

Applications of Saliency Models

What do you wish to see? A summarization system for movies based on user preferences

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