A novel clustering method for static video summarization

Wu, Jiaxin; Zhong, Sheng-hua; Jiang, Jianmin; Yang, Yunyun

doi:10.1007/s11042-016-3569-x

Cited by 85 publications

(37 citation statements)

References 21 publications

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“…We evaluate the performances of the proposed cryptosystem using various images [35,36] with Matlab (R2017b). Based on the results of the related work [11], the precision of extracting the keyframes are varied depending on the databases. e efficiency of extracting the keyframes depends on the surveillance video.…”

Section: Discussionmentioning

confidence: 99%

“…To overcome these tackles, we employ a video summarization technique based on the K-means algorithm and histogram values of the frames. A fast data extraction based on an automatic video summarization technique is proposed in this part based on the related work [11]. e aim is to detect the important frames and preview a summarized version of a long video.…”

Section: Keyframes Extraction Technique Dissemination Of Videomentioning

confidence: 99%

“…Recently, several research studies have been proposed to extract keyframes based on different techniques such as the clustering algorithm [11].…”

Section: Keyframes Extraction Technique Dissemination Of Videomentioning

confidence: 99%

“…On the other hand, the surveillance system generates a huge amount of data that require realtime processing. To deal with computational complexity, some researchers propose several contributions such as video summarization techniques [11,12]. e main task of these techniques is to summarize the most informative parts of video data.…”

Section: Introductionmentioning

confidence: 99%

“…is paper proposes a cryptosystem framework based on the summarization technique using compression and encryption schemes. A video summarization technique is used to identify the informative keyframes and reduce the video redundancy based on the presented technique in Wu et al [11]. Accordingly, we compress the extracted frames by applying DCT.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

et al. 2019

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Surveillance systems paradigm envisions the pervasive interconnection and cooperation of interactive devices over the Internet infrastructure. Nevertheless, dissemination and processing of surveillance video amid the Internet of Things (IoT) applications become a susceptible issue due to the large volume and the significant information of these data. Moreover, surveillance devices on IoT have very limited resources such as memory and storage. The actual security methods are not quite appropriate for surveillance IoT systems. Thus, a particular cryptosystem technique is required for surveillance data security. In this paper, we propose an efficient cryptosystem to secure IoT-based surveillance systems. The proposed cryptosystem framework contains three parts. First, a lightweight automatic summarization technique based on a fast histogram-clustering approach is used to extract the keyframes from the surveillance video. Then, we employ a discrete cosine transform (DCT) technique to compress the extracted data size. Finally, the proposed framework performs an efficient image encryption algorithm by employing a discrete fractional random transform (DFRT). The testing results and analysis confirm the features of the proposed cryptosystem on surveillance systems. The proposed framework is fast and ensures secure and efficient real-time processing by minimizing the transmission cost and storage.

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Section: Discussionmentioning

confidence: 99%

Section: Keyframes Extraction Technique Dissemination Of Videomentioning

confidence: 99%

“…Recently, several research studies have been proposed to extract keyframes based on different techniques such as the clustering algorithm [11].…”

Section: Keyframes Extraction Technique Dissemination Of Videomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

et al. 2019

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Enhanced Block‐Based Feature Agglomeration Clustering for Video Summarization

Sreeja

Kovoor

2021

Machine Learning Algorithms and Applications

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Network video summarization based on key frame extraction via superpixel segmentation

Jin

et al. 2020

Trans Emerging Tel Tech

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The spread of insecure online video has been a serious social problem. The video summarization becomes one of key step for automatic filtering the expected video from the Internet. At present, the most existing video summarization methods are based on calculating the image similarity between video frames, so that the key frame can be selected properly. In this article, we introduce a superpixel segmentation based image similarity calculation, and then the metric is applied into video summarization. To identify the video key frames, we introduce superpixel segmentation to cluster the pixels locally by estimating the optical flow displacement field between successive frames, which can extract key frames and reduce video redundancy. On the VSUMM dataset and YouTube dataset, the experimental results demonstrate that the proposed method has clear advantages on both subjectively qualitative analysis and objectively quantitative evaluation comparing with the state of the art methods. INTRODUCTIONWith the popularization of the Internet, network security is becoming more and more important. With the gradual maturity of video capture technologies, huge numbers of digital video has emerged in each day. As the main multimedia information carrier, the digital video appears in all aspects of our daily life in the form of drama, news, sports, and surveillance. These massive videos enable people to obtain information in richer forms and bring great convenience to life. However, it has a huge impact on network security, which is an important issue related to national security, social stability, inheritance, and development of national culture. It also puts tremendous pressure on video storage, transmission, archiving, and retrieval. Facing vast video data, the video summarization technique enables us extracting the key contents without viewing all the video content. This technique is not only just giving us a quick view ability but also helping us on increasing storage density and ensuring network security. [1][2][3] At present, various algorithms are proposed to eliminate the redundancy of the original video by selecting and combining representative and meaningful portions of the video. The video summary problem was first introduced in 1994 at Carnegie Mellon University. 4 Since then, many researchers have joined in video summarization, which has already been improved significantly. According to the output, the current video summary technology can be roughly divided into two categories: dynamic video summary algorithm 5 and static video summary algorithm. 6 This article focuses on the static video summary based on key frames extraction, which is selecting the set of static key frames by optimizing the diversity or representativeness of the output.

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A novel clustering method for static video summarization

Cited by 85 publications

References 21 publications

An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

Enhanced Block‐Based Feature Agglomeration Clustering for Video Summarization

Network video summarization based on key frame extraction via superpixel segmentation

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