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video summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

Computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang Department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer Science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

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Shot classification and replay detection for sports video summarization

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Frontiers of Information Technology & Electronic Engineering 2022年第5期23卷 790-800页

作者： Ali JAVED Amen ALI KHAN Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan

Automated analysis of sports video summarization is challenging due to variations in cameras,replay speed,illumination conditions,editing effects,game structure,genre,etc.To address these challenges,we propose an effective video summarization framework based on shot classification and replay detection for field sports videos.Accurate shot classification is mandatory to better structure the input video for further processing,i.e.,key events or replay detection.Therefore,we present a lightweight convolutional neural network based method for shot classification.Then we analyze each shot for replay detection and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos.For this purpose,we propose local octa-pattern features to represent video frames and train the extreme learning machine for classification as replay or non-replay frames.The proposed framework is robust to variations in cameras,replay speed,shot speed,illumination conditions,game structure,sports genre,broadcasters,logo designs and placement,frame transitions,and editing effects.The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer,baseball,and cricket.Experimental results demonstrate that the proposed framework can reliably be used for shot classification and replay detection to summarize field sports videos.

关键词： Extreme learning machine Lightweight convolutional neural network Local octa-patterns Shot classification Replay detection video summarization

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Effective video summarization Approach Based on Visual Attention

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Computers, Materials & Continua 2022年第4期71卷 1427-1442页

作者： Hilal Ahmad Habib Ullah Khan Sikandar Ali Syed Ijaz Ur Rahman Fazli Wahid Hizbullah Khattak Department of Computer Sciences Islamia College Peshawar KhyberPakhtunkhwaPakistan Department of Accounting and Information Systems College of Business and EconomicsQatar UniversityDoha2713Qatar Department of Information Technology The University of HaripurKhyber PakhtunkhwaPakistan Department of Information Technology Hazara University MansehraKhyber PakhtunkhwaPakistan

video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the frames that stand out visually are extracted as key frames based on humanattention modeling theories. The schemes for modeling visual attention haveproven to be effective for video summaries. Nevertheless, the high cost ofcomputing in such techniques restricts their usability in everyday situations.In this context, we propose a method based on KFE (key frame extraction)technique, which is recommended based on an efficient and accurate visualattention model. The calculation effort is minimized by utilizing dynamicvisual highlighting based on the temporal gradient instead of the traditionaloptical flow techniques. In addition, an efficient technique using a discretecosine transformation is utilized for the static visual salience. The dynamic andstatic visual attention metrics are merged by means of a non-linear weightedfusion technique. Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy. The experimental resultsof our proposed model indicate the efficiency and high standard in terms ofthe key frames extraction as output.

关键词： KFE video summarization visual saliency visual attention model

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An Efficient Method for Underwater video summarization and Object Detection Using YoLoV3

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Intelligent Automation & Soft Computing 2023年第2期35卷 1295-1310页

作者： Mubashir Javaid Muazzam Maqsood Farhan Aadil Jibran Safdar Yongsung Kim Department of Computer Science UETTaxilaPakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Technology Education Chungnam National UniversityDaejeon34134Korea

Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect videos.However,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant information.Hence,to automate this manual procedure of video assessment,an accurate and efﬁcient automated system is a greater necessity.From this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy(PME)method toﬁrst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to theﬁeld of underwater archaeology,stock assessment,and surveillance.

关键词： Computer vision deep learning digital image processing underwater video analysis video summarization object detection YOLOV3

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video summarization with a graph convolutional attention network

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Frontiers of Information Technology & Electronic Engineering 2021年第6期22卷 902-913页

作者： Ping LI Chao TANG Xianghua XU School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China The State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

video summarization has established itself as a fundamental technique for generating compact and concise video, which alleviates managing and browsing large-scale video data. Existing methods fail to fully consider the local and global relations among frames of video, leading to a deteriorated summarization performance. To address the above problem, we propose a graph convolutional attention network(GCAN) for video summarization. GCAN consists of two parts, embedding learning and context fusion, where embedding learning includes the temporal branch and graph branch. In particular, GCAN uses dilated temporal convolution to model local cues and temporal self-attention to exploit global cues for video frames. It learns graph embedding via a multi-layer graph convolutional network to reveal the intrinsic structure of frame samples. The context fusion part combines the output streams from the temporal branch and graph branch to create the context-aware representation of frames, on which the importance scores are evaluated for selecting representative frames to generate video summary. Experiments are carried out on two benchmark databases, Sum Me and TVSum, showing that the proposed GCAN approach enjoys superior performance compared to several state-of-the-art alternatives in three evaluation settings.

关键词： Temporal learning Self-attention mechanism Graph convolutional network Context fusion video summarization

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Text Semantics Based Automatic summarization for Chinese videos

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Chinese Journal of Electronics 2015年第3期24卷 462-467页

作者： WANG Xingqi ZHA Taotao WU Chunming FANG Jinglong JIANG Ming School of Computer Science Hangzhou Dianzi University College of Computer Science and Technology Zhejiang University

This paper is concerned with the task of automatically summarizing videos, which is important for many video-related applications. Examples include video retrieval, accessing and categorization, et al. Our approach summarizes video semantically from the texts embedded in videos. To improve the accuracy, ant colony algorithm,maximum matching method, Singular value decomposition(SVD) are employed for locating text region, segmenting words and clustering sentences, respectively. Based on these methods, a prototype is developed. In an experimental evaluation on the real world video datasets, we show that our proposed approach could provide accurate, satisfactory performance.

关键词： video summarization Ant colony algorithm Maximum matching method Singular value decomposition(SVD)

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Interactive multi-scale structures for summarizing video content

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Science China(Information Sciences) 2013年第5期56卷 103-114页

作者： WANG HongAn MA CuiXia State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences

Efficient video summarization leads to facilely exploring video content appropriate to the user’s intention with low cognitive demand.In this paper,we present a novel approach for summarizing videos in the form of multi-scale structures that exhibit different video features at different scale levels and allow exploration of video contents with multi-scale interaction.The semantic relationship between structures is addressed and user intention is also considered and integrated in the summarization and interaction.This paper first introduces the concept of multi-scale structures for summarizing video content and describes three different types of structures that present important features at different scale levels.Furthermore,a continuous zooming interaction for browsing multi-scale structures is provided to facilitate video browsing.Finally,an elaborate user study is conducted showing that user performance on understanding and browsing videos is improved.

关键词： multi-scale video summarization sketch visualization interaction

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