Extraction of User Preference for Video Stimuli Using EEG‐Based User Responses

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)‐based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic‐discriminant‐analysisbased model using BP features achieves a classification accuracy of 97.39% , and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.     

基于拉格朗日高斯变换的奥运会视频分类机制研究

随着现代体育不断发展,奥运会承办比赛种类越 来越繁杂,对赛事视频分类提出了一 个新的挑战。现有的人工分类方法无法有效地区分团体竞技类比赛(球类)和个人竞技类比 赛(田径类)视频,从而进行大规模自动分类存储。然而,为了有效地重复使用这些视频文 件,需要对其进行分类存储,主要目的在于提高资源的利用率。针对人工分类手段太过于低 效的现状,本文对奥运会运动视屏内容分类问题进行研究,并提出了一种基于关键帧特征提 取和支持向量机(Supported Vector Machine,SVM)的视频分类方法。以第31届奥运会的 体育视频作为数据集,对每个视频进行关键帧提取和总结,并借由拉格朗日-高斯变换来计 算视频对应的特征向量,将特征向量作为SVM分类器的输入进行体育视频分类。实验结果表 明,对于任意奥运视频,提出的方法平均能够取得70%以上的正确分类率,而错误分类的比 例始终低于10%。特别地,对于奥运中的射击类视频,平均正确分类 率接近90%左右,说明了提出方法的有效性。     

An HMM based analysis framework for semantic video events

Semantic video analysis plays an important role in the field of machine intelligence and pattern recognition. In this paper, based on the Hidden Markov Model （HMM）, a semantic recognition framework on compressed videos is proposed to analyze the video events according to six low-level features. After the detailed analysis of video events, the pattern of global motion and five features in foreground-the principal parts of videos, are employed as the observations of the Hidden Markov Model to classify events in videos. The applications of the proposed framework in some video event detections demonstrate the promising success of the proposed framework on semantic video analysis.     

The (Non)Violent World of YouTube: Content Trends in Web Video

In this content analysis, we examined violence in Web‐based entertainment. YouTube videos (N = 2,520) were collected in 3 different categories: most viewed, top rated, and random, with additional comparisons between amateur and professional content. Frequencies of violent acts and the context of violence (e.g., characteristics of perpetrator and victim, justification, consequences) were compared both between these categories of YouTube videos and with existing research on television violence. The results showed far less violence as a percentage of programming on YouTube than there is on television. Moreover, the violence that was present showed more realistic consequences and more negative context than television violence. Post hoc comparisons illustrated several differences in the presentation of violence between make and category of video.     

基于多种上下文结构与线性融合的特定敏感视频识别

本文中特定敏感视频是指恐怖和暴力视频,现有的特定敏感视频识别算法或是忽略了视频的多种上下文结构信息;或是忽略了各种特征间潜在的依赖关系.因此,本文提出了一种基于多种上下文结构与线性融合的特定敏感视频识别方法,首先针对某种视频提取多种有效特征,并获取镜头间的上下文结构信息;然后,在每一个特征空间中利用上下文结构训练一个SVM分类器;最后,获取不同特征间的依赖关系,采用线性依赖模型融合多个分类器的结果,提高视频的识别率.在特定敏感视频库上的实验结果验证了该方法比现有的其它算法有更好的性能和稳定性.     

基于内容的视频语义分析关键技术

分析了视频数据与文本数据的差异,以及视频数据在视频分析检索方面存在的问题。从视频内容分析领域的研究热点出发,分别对视频语义库、与视频分析相关的视频低层特征、视频对象划分与识别、视频信息描述与编码等方面的技术进行了分析和对比。并提出了一个视频语义分析的框架和分析流程。     

动态背景下融合运动线索和颜色信息的视频目标分割算法

针对现有动态背景下目标分割算法存在的局限性,提出了一种融合运动线索和颜色信息的视频序列目标分割算法。首先,设计了一种新的运动轨迹分类方法,利用背景运动的低秩特性,结合累积确认的策略,可以获得准确的运动轨迹分类结果;然后,通过过分割算法获取视频序列的超像素集合,并计算超像素之间颜色信息的相似度;最后,以超像素为节点建立马尔可夫随机场模型,将运动轨迹分类信息以及超像素之间颜色信息统一建模在马尔可夫随机场的能量函数中,并通过能量函数最小化获得每个超像素的最优分类。在多组公开发布的视频序列中进行测试与对比,结果表明,本文方法可以准确分割出动态背景下的运动目标,并且较传统方法具有更高的分割准确率。     

Combining SVM and CHMM classifiers for porno video recognition

Porno video recognition is important for Internet content monitoring.In this paper,a novel porno video recognition method by fusing the audio and video cues is proposed.Firstly,global color and texture...     

Bimodal fusion of low-level visual features and high-level semantic features for near-duplicate video clip detection

The detection of near-duplicate video clips (NDVCs) is an area of current research interest and intense development. Most NDVC detection methods represent video clips with a unique set of low-level visual features, typically describing color or texture information. However, low-level visual features are sensitive to transformations of the video content. Given the observation that transformations tend to preserve the semantic information conveyed by the video content, we propose a novel approach for identifying NDVCs, making use of both low-level visual features (this is, MPEG-7 visual features) and high-level semantic features (this is, 32 semantic concepts detected using trained classifiers). Experimental results obtained for the publicly available MUSCLE-VCD-2007 and TRECVID 2008 video sets show that bimodal fusion of visual and semantic features facilitates robust NDVC detection. In particular, the proposed method is able to identify NDVCs with a low missed detection rate (3% on average) and a low false alarm rate (2% on average). In addition, the combined use of visual and semantic features outperforms the separate use of either of them in terms of NDVC detection effectiveness. Further, we demonstrate that the effectiveness of the proposed method is on par with or better than the effectiveness of three state-of-the-art NDVC detection methods either making use of temporal ordinal measurement, features computed using the Scale-Invariant Feature Transform (SIFT), or bag-of-visual-words (BoVW). We also show that the influence of the effectiveness of semantic concept detection on the effectiveness of NDVC detection is limited, as long as the mean average precision (MAP) of the semantic concept detectors used is higher than 0.3. Finally, we illustrate that the computational complexity of our NDVC detection method is competitive with the computational complexity of the three aforementioned NDVC detection methods.     

A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN

Video semantic detection has been one research hotspot in the field of human-computer interaction. In video features-oriented sparse representation, the features from the same category video could not achieve similar coding results. To address this, the Locality-Sensitive Discriminant Sparse Representation (LSDSR) is developed, in order that the video samples belonging to the same video category are encoded as similar sparse codes which make them have better category discrimination. In the LSDSR, a discriminative loss function based on sparse coefficients is imposed on the locality-sensitive sparse representation, which makes the optimized dictionary for sparse representation be discriminative. The LSDSR for video features enhances the power of semantic discrimination to optimize the dictionary and build the better discriminant sparse model. More so, to further improve the accuracy of video semantic detection after sparse representation, a weighted K-Nearest Neighbor (KNN) classification method with the loss function that integrates reconstruction error and discrimination for the sparse representation is adopted to detect video semantic concepts. The proposed methods are evaluated on the related video databases in comparison with existing sparse representation methods. The experimental results show that the proposed methods significantly enhance the power of discrimination of video features, and consequently improve the accuracy of video semantic concept detection.     

Deep multi-query video retrieval

Video retrieval methods have been developed for a single query. Multi-query video retrieval problem has not been investigated yet. In this study, an efficient and fast multi-query video retrieval framework is developed. Query videos are assumed to be related to more than one semantic. The framework supports an arbitrary number of video queries. The method is built upon using binary video hash codes. As a result, it is fast and requires a lower storage space. Database and query hash codes are generated by a deep hashing method that not only generates hash codes but also predicts query labels when they are chosen outside the database. The retrieval is based on the Pareto front multi-objective optimization method. Re-ranking performed on the retrieved videos by using non-binary deep features increases the retrieval accuracy considerably. Simulations carried out on two multi-label video databases show that the proposed method is efficient and fast in terms of retrieval accuracy and time.     

Classification of Degradation Types Based on Distribution of Blocky Blocks for IP‐Based Video Services

In this letter, we propose a new quality measurement method to identify the causes of video quality degradation for IP‐based video services. This degradation mainly results from network performance issues and video compression. The proposed algorithm identifies the causes based on statistical feature values from blocky block distribution in degraded IP‐based videos. We found that the sensitivity and specificity of the proposed algorithm are 93.63% and 91.99%, respectively, in comparison with real error types and subjective test data.     

Data hiding in partially encrypted HEVC video

In this study, an efficient scheme for hiding data directly in partially encrypted versions of high efficiency video coding (HEVC) videos is proposed. The content owner uses stream cipher to selectively encrypt some HEVC‐CABAC bin strings in a format‐compliant manner. Then, the data hider embeds the secret message into the encrypted HEVC videos using the specific coefficient modification technique. Consequently, it can be used in third‐party computing environments (more generally, cloud computing). For security and privacy purposes, service providers cannot access the visual content of the host video. As the coefficient is only slightly modified, the quality of the decrypted video is satisfactory. The encrypted and marked bitstreams meet the requirements of format compatibility, and have the same bit rate. At the receiving end, data extraction can be performed in the encrypted domain or decrypted domain that can be adapted to different application scenarios. Several standard video sequences with different resolutions and contents have been used for experimental evaluation.     

A Personal Videocasting System with Intelligent TV Browsing for a Practical Video Application Environment

In this paper, a video broadcasting system between a home‐server‐type device and a mobile device is proposed. The home‐server‐type device can automatically extract semantic information from video contents, such as news, a soccer match, and a baseball game. The indexing results are utilized to convert the original video contents to a digested or arranged format. From the mobile device, a user can make recording requests to the home‐server‐type devices and can then watch and navigate recorded video contents in a digested form. The novelty of this study is the actual implementation of the proposed system by combining the actual IT environment that is available with indexing algorithms. The implementation of the system is demonstrated along with experimental results of the automatic video indexing algorithms. The overall performance of the developed system is compared with existing state‐of‐the‐art personal video recording products.     

基于DCT系数统计特性的HEVC视频双压缩检测算法

功能强大和使用简易的视频编辑软件可能会使数字视频遭受到各种不同形式的篡改,视频的真实性和完整性无法得到保证。双压缩是视频篡改的必要条件,双压缩检测则是视频取证的重要辅助手段。通过分析压缩过程中由量化误差引起的离散余弦变换(DCT)系数变化,提出了一种不同量化参数下的高效视频编码(HEVC)视频双压缩检测算法,利用DCT系数直方图和相邻DCT系数对奇偶组合统计特性构造22维联合特征集,最后将特征集用支持向量机(SVM)进行分类识别。实验结果证明了本文算法的有效性。     

一种基于加速近端梯度法的视频散列算法研究

散列算法已经被广泛应用于视频数据的索引。然而,当前大多数视频散列方法将视频看成是多个独立帧的简单集合,通过综合帧的索引来对每个视频编制索引,在设计散列函数时往往忽略了视频的结构信息。首先将视频散列问题建模为结构正规化经验损失的最小化问题。然后提出一种有监管算法,通过利用结构学习方法来设计高效的散列函数。其中,结构正规化利用了出现于视频帧(与相同的语义类别存在关联)中的常见局部视觉模式,同时对来自同一视频的后续帧保持时域一致性。证明了通过使用加速近端梯度(APG)法可有效求解最小化目标问题。最后,基于两个大规模基准数据集展开全面实验(150 000个视频片断,1 200万帧),实验结果证明了该方法性能优于当前其他算法。     

A framework for video event classification by modeling temporal context of multimodal features using HMM

Semantic high-level event recognition of videos is one of most interesting issues for multimedia searching and indexing. Since low-level features are semantically distinct from high-level events, a hierarchical video analysis framework is needed, i.e., using mid-level features to provide clear linkages between low-level audio-visual features and high-level semantics. Therefore, this paper presents a framework for video event classification using temporal context of mid-level interval-based multimodal features. In the framework, a co-occurrence symbol transformation method is proposed to explore full temporal relations among multiple modalities in probabilistic HMM event classification. The results of our experiments on baseball video event classification demonstrate the superiority of the proposed approach.     

Gesture recognition for human–machine interaction in table tennis video based on deep semantic understanding

The analysis of moving objects in videos, especially the recognition of human motions and gestures, is attracting increasing emphasis in computer vision area. However, most existing video analysis methods do not take into account the effect of video semantic information. The topological information of the video image plays an important role in describing the association relationship of the image content, which will help to improve the discriminability of the video feature expression. Based on the above considerations, we propose a video semantic feature learning method that integrates image topological sparse coding with dynamic time warping algorithm to improve the gesture recognition in videos. This method divides video feature learning into two phases: semi-supervised video image feature learning and supervised optimization of video sequence features. Next, a distance weighting based dynamic time warping algorithm and K-nearest neighbor algorithm is leveraged to recognize gestures. We conduct comparative experiments on table tennis video dataset. The experimental results show that the proposed method is more discriminative to the expression of video features and can effectively improve the recognition rate of gestures in sports video.     

基于多特征的视频内容安全过滤方法

应国家对视频网站加强有序管理的迫切要求,文中应用一种基于多模态特征的网络视频分类方法,实现对网络视频的安全监管。该方法对从网络视频中提取三大类的视频特征,分别针对音频特征、运动和颜色以及空间和时间特征,递进地对视频进行过滤。通过对视频中不良场景的定义,包括恐怖、暴力和色情语义,以检测网络视频内容中潜在的不良信息,实验证明该方法有效地提高了不良视频检测和分类的准确率。