Salient object extraction for user-targeted video content association

The increasing amount of videos on the Internet and digital libraries highlights the necessity and importance of interactive video services such as automatically associating additional materials(e.g.,advertising logos and relevant selling information) with the video content so as to enrich the viewing experience.Toward this end,this paper presents a novel approach for user-targeted video content association(VCA) .In this approach,the salient objects are extracted automatically from the video stream using complementary saliency maps.According to these salient objects,the VCA system can push the related logo images to the users.Since the salient objects often correspond to important video content,the associated images can be considered as content-related.Our VCA system also allows users to associate images to the preferred video content through simple interactions by the mouse and an infrared pen.Moreover,by learning the preference of each user through collecting feedbacks on the pulled or pushed images,the VCA system can provide user-targeted services.Experimental results show that our approach can effectively and efficiently extract the salient objects.Moreover,subjective evaluations show that our system can provide content-related and user-targeted VCA services in a less intrusive way.

Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

VideoMap: An interactive and scalable visualization for exploring video content

Large-scale dynamic relational data visualization has attracted considerable research attention recently. We introduce dynamic data visualization into the multimedia domain, and present an interactive and scalable system, Video Map, for exploring large-scale video content. A long video or movie has much content; the associations between the content are complicated. Video Map uses new visual representations to extract meaningful information from video content. Map-based visualization naturally and easily summarizes and reveals important features and events in video. Multi-scale descriptions are used to describe the layout and distribution of temporal information, spatial information, and associations between video content. Firstly, semantic associations are used in which map elements correspond to video contents. Secondly, video contents are visualized hierarchically from a large scale to a fine-detailed scale. Video Map uses a small set of sketch gestures to invoke analysis, and automatically completes charts by synthesizing visual representations from the map and binding them to the underlying data. Furthermore,Video Map allows users to use gestures to move and resize the view, as when using a map, facilitating interactive exploration. Our experimental evaluation of Video Map demonstrates how the system can assist in exploring video content as well as significantly reducing browsing time when trying to understand and find events of interest.

Shearlet Based Video Fingerprint for Content-Based Copy Detection

Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.

Joint Content Placement and Traffic Management for Cloud Mobile Video Distribution over Software-Defined Networks

To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking(SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

Content-Based Hierarchical Analysis of News Video Using Audio and Visual Information

A schema for content-based analysis of broadcast news video is presented. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

NewsVideoCAR:一个基于内容的视频新闻节目浏览检索系统

介绍了ＮｅｗｓＶｉｄｅｏＣＡＲ系统的构成，核心技术的基本思想和浏览界面的设计要点．

Pano Video:摄像机运动建模及从视频估计摄像机运动参数的一种方法

通过给摄像机平移、旋转与变焦等运动建模，并把运动模型与基于象素点亮度变化的方法相结合来估计摄像机运动参数。用得到的运动参数可以把视频构造成一幅全景图，全景图可广泛应用于视频压缩与检索。实验表明，该方法可成功地应用于视频会议系统中的视频压缩与视频检索。

Recent Advances and Challenges in Video Quality Assessment

Video quality assessment(VQA) plays a vital role in the field of video processing, including areas of video acquisition, video filtering in retrieval, video compression, video restoration, and video enhancement. Since VQA has gained much attention in recent years, this paper gives an up-to-date review of VQA research and highlights current challenges in this filed. The subjective study and common VQA databases are first reviewed.Then, a survey on the objective VQA methods, including full-reference, reduced-reference,and no-reference VQA, is reported. Last but most importantly, the key limitations of current research and several challenges in the field of VQA are discussed, which include the impact of video content, memory effects, computational efficiency, personalized video quality prediction, and quality assessment of newly emerged videos.

Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values

Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify interframe forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.

Behind the Scenes with a Video Training Platform: The Challenges of Researching the Provision of Open Educational Resources

Video training platforms are now being implemented on a large scale in organizations. In this paper, I look at a video training platform including open educational resources available for many employees with varying patterns and motivations for use. This has provided me with a research challenge to find methods that help other practitioners in the field understand and explain such initiatives. I describe ways to model the research and identify where pressures and contradictions can be found, drawing on a reflective view of my own practice in performing the research. Open educational resources are defined as technology-enabled educational resources that are openly available for consultation, use and adaptation by users for non-commercial purposes [1]. The bank subject to this case study has been the first organisation in Turkey that provided open educational resources for all its employees. The video platform (called “For @ Tube”) provides users with over 100 video lectures drawn from reputable universities around the world including Yale and Harvard. Other learning tools such as discussion forums, blogs and traditional e-learning courses have been made available to the users on the e-learning platform called “For @” since 2006. In this paper, I aim to introduce the new video training platform (“For @ Tube”) and outline some of the main research issues surrounding such an initiative. I seek to explore theoretical and practical approaches that can provide suitable tools for analysis. Activity theory is seen as a suitable approach for macroanalysis and its use is illustrated in terms of the complexity of large scale research. Activity theory, besides informing research perspectives, can be turned in upon the research process itself, allowing us to consider the challenges and context of the research. By using activity theory in this way and illustrating from a range of practical approaches, I demonstrate and illustrate a useful research approach.

Screen Content Coding with Primary and Secondary Reference Buffers for String Matching and Copying

A screen content coding （SCC） algorithm that uses a primary reference buffer （PRB） and a secondary reference buffer （SRB） for string matching and string copying is proposed. PRB is typically the traditional reconstructed picture buffer which provides reference string pixels for the current pixels being coded. SRB stores a few of recently and frequently referenced pixels for repetitive reference by the current pixels being coded. In the encoder, searching of optimal reference string is performed in both PRB and SRB, and either a PRB or SRB string is selected as an optimal reference string on a string-by-string basis. Compared with HM-16.4＋SCM-40 reference software, the proposed SCC algorithm can improve coding performance measured by bit-distortion rate reduction of average 4.19% in all-intra configuration for text and graphics with motion category＇ of test sequences defined by JCT-VC common test condition.

涉Sora等文生视频危害行为的刑法规制

Sora等文生视频的应用将对刑法的法益理论产生重大影响。应将文生视频生成内容纳入作品范围。文生视频是其生成内容的作者但尚不是权利主体,应将相关著作财产权转移给使用人。涉文生视频强制猥亵、侮辱罪所侵害法益应当包括精神损害的内容。文生视频的应用改变了视频创作行业的社会关系,进而会改变相关犯罪行为法益侵害性程度的判断标准。涉文生视频犯罪主要涉及侵犯人身权利类犯罪、侵犯财产权利类犯罪以及寻衅滋事犯罪。司法解释对涉文生视频犯罪相关罪名构成要件的规定应当既有突破也有限制。我国刑法应设立数据安全责任事故罪以追究文生视频研发者、生产者的过失罪过下的刑事责任。应及时完善生成式人工智能产品应用的前置法规定。司法实践中应将文生视频的智能化程度区分为信息的整理、信息的基本加工、信息的深度加工三类,以此合理分配使用者、研发者与生产者所应当承担的涉文生视频犯罪的刑事责任。

融合知识图谱的影视视频标签分类算法研究

鉴于视频感知方式的多样性,视频标签层级分类算法均从视觉和文本模态入手,训练联合模型共同推断视频内容。但现有研究通常只适用于粗粒度的分类,针对影视剧名的分类,则需要更加细粒度的识别。提出了一个融合知识图谱的影视视频标签分类算法。首先,使用了基于大规模通用数据训练的多模态预训练模型提取了视觉和文本的特征,训练了一个多任务的视频标签预测模型,得到视频的类型、题材和实体三级标签;通过在多任务学习网络中引入相似性任务提高分类模型训练的难度,使得同类样本特征更加紧密,且更好地表达样本差异。其次,对于最细粒度的实体标签,提出了一个局部注意力头扩展的实体纠错模型,引入外部知识图谱的共现信息对前置模型的预测结果做修正,得到更准确的实体标签预测结果。采集豆瓣的半结构化数据构建了影视知识图谱并对影视视频标签分类模型进行了实证研究。视频标签分类的实验结果表明,首先,基于多任务网络结构,在训练分类任务时加入交叉熵损失函数和相似性损失函数对模型进行共同约束优化了特征表达,在类型、题材、实体标签的Top-1分类准确率上分别提升了3.70%、3.35%和16.57%;其次,针对前置模型的困难样本提出的全局-局部注意力机制模型,在引入了知识图谱信息之后,实体标签的Top-1分类准确率从38.7%提升到45.6%。该研究是使用图片-文本对数据在多模态视频标签分类问题上新的尝试,为少量数据样本情况下的短视频标签分类提供了新的研究思路。

短视频内容营销对食品购买意愿的影响研究:以情绪为调节变量

数字媒体时代,用户逐渐从被动接受信息转向主动获取信息,决策机制、内容营销的路径和思维都已经发生悄然改变。短视频创作内容缺乏新颖性、企业价值认知不充分、平台监测机制不清晰等问题也随之显现。基于SOR模型构建短视频内容营销对食品购买意愿的影响模型,开展问卷调查并进行实证分析。分析结果表明,产品因素、情景因素、感知风险、感知价值、感知愉悦性对食品购买意愿具有显著性影响,感知风险、感知价值和感知愉悦性在情景因素对食品购买意愿的影响中发挥中介作用,情绪在情景因素对食品购买意愿的影响中发挥调节作用。基于研究结论提出如下建议:充分利用传播媒介,持续输出优质短视频内容;把握消费者诉求,精准输出个性化短视频内容;完善产品质量把控,提升消费者感知;把握消费者情绪,激发潜在需求;创作情感标签鲜明的短视频,激发情感共鸣。

全民视频时代下微课与知识分享类视频对比研究

随着视频技术的普及和互联网的迅猛发展,视频在教育领域的应用已成为一种重要的趋势。该文深入探讨了微课和知识分享类短视频这两种视频教育形式,在全民视频时代的发展轨迹、独特特性及它们之间的优势和不足。通过细致地对比分析,文章揭示了这两种形式在内容深度、创作者资质、受众偏好和教育成效等方面的区别。微课因其系统性和专业性,适合深度学习;而知识分享类短视频则以其便捷性和趣味性,吸引更广泛的受众。该文进一步预测,这两种视频形式在未来可能会实现互补和融合,共同促进教育内容的创新和教学方式的革新,为学习者提供更加丰富多元的学习体验,推动教育行业的持续进步和转型。

教学视频角色表征对学习影响的多模态数据融合分析

当前,教学视频已成为学生日常学习过程中不可或缺的数字学习资源,其设计与优化受到了研究者的广泛关注。但相关研究较少从教学媒体角色表征的角度出发,对不同类型的教学视频进行比较研究。为此,文章基于拟剧理论和信息技术3T论,以广东省H大学教育技术学专业的90名大学生为实验对象,设置了三组表征不同角色(教师、工具、同伴)的教学视频,通过对眼动注视数据、视频画面数据、学习成绩数据等多模态数据的融合分析,发现表征不同角色的视频对学生的学习体验和学习效果有显著影响,使用表征教师角色、同伴角色的视频学习能带来更好的学习体验和学习效果。同时,文章对不同角色表征的视频与学习效果之间的中介效应进行了分析,发现总注视时长在不同角色组教学视频与学习效果之间起中介作用,而心流体验对学习效果没有中介作用。文章的研究对于指导学生选择学习资源、优化教学设计、提升教学效果有一定的实践价值,可为数字化学习环境下的教学实践提供有益参考。