Video scene clustering by graph partitioning

A new video scene clustering method using graph partitioning is proposed. In the method, the shots of a video are grouped into clusters of similar scenes based on shot colour attributes; the number of scene clusters is not required to be known a priori. Experimental results are presented to show the effectiveness of the proposed method.     

Dominant sets based movie scene detection

Multimedia indexing and retrieval has become a challenging topic in organizing huge amount of multimedia data. This problem is not a trivial task for large visual databases; hence, segmentation into low- and high-level temporal video segments might improve the realization of this task. In this paper, we introduce a weighted undirected graph-based movie scene detection approach to detect semantically meaningful temporal video segments. The method is based on the idea of finding the dominant scene of the video according to the selected low-level feature. The proposed method starts from obtaining the most reliable solution first and exploit each solution in the subsequent steps recursively. The dominant movie scene boundary, which can be the highest probability to be the correct one, is determined and this scene boundary information is also exploited in the subsequent steps. We handle two partitioning strategies to determine the boundaries of the remaining scenes. One is a tree-based strategy and the other is an order-based strategy. The proposed dominant sets based movie scene detection method is compared with the graph-based video scene detection methods presented in literature.     

基于类内和类间损失的场景构造方法

为了便于在不同结构层次上对视频进行检索和浏览。可以把视频序列分为不同层次的逻辑单元。逻辑单元的层次由上到下可以分成序列、场景、镜头和帧。其中场景是时间上具有一定顺序关系的相似镜头的集合。文章提出了基于类内和类间损失的场景构造算法。首先利用时间约束和颜色直方图求得镜头距离；然后基于类内和类间损失对相似镜头进行聚类，得到镜头类；最后在分析镜头类的基础上构造场景。实验证明，构造的场景比较好的反映了视频的内容。     

A motion and illumination resilient framework for automatic shot boundary detection

Detecting and locating a desired information in hefty amount of video data through manual procedure is very cumbersome. This necessitates segregation of large video into shots and finding the boundary between the shots. But shot boundary detection problem is unable to achieve satisfactory performance for video sequences consisting of flash light and complex object/camera motion. The proposed method is intended for recognising abrupt boundary between shots in the presence of motion and illumination change in an automatic way. Typically any scene change detection algorithm assimilates time separation in a shot resemblance metric. In this communication, absolute sum gradient orientation feature difference is matched to automatically generated threshold for sensing a cut. Experimental study on TRECVid 2001 data set and other publicly available data set certifies the potentiality of the proposed scheme that identifies scene boundaries efficiently, in a complex environment while preserving a good trade-off between recall and precision measure.     

用非监督式聚类进行视频镜头分割

镜头边界检测是基于内容的视频检索首先要解决的问题。研究人员通常将镜头转换分为突变和渐变,并根据各种转换的特点采用不同的检测算法。在研究中发现,视频镜头的抽象程度与对其进行边界划分的精度相关。为此,提出采用非监督式聚类算法,按照给定的相似尺度对视频数据进行自组织和动态分析,完成层次化的镜头分割。该算法侧重于揭示视频的层次结构,能实现不同精度的视频抽象要求。     

武打片中的动作场景检测方法

本文提出了一种简单有效的方法检测武打片中的动作场景:首先根据动作场景的节奏特点,从影片层次出发,使用镜头长度和MPEG-7运动活力描述符定义了镜头的步调函数来度量节奏,由此定位快节奏区域,找到动作场景的大体位置;之后根据动作场景的内容发展特点,从镜头层次出发,分析快节奏区域及周边的镜头的内容,根据视觉特征确定动作场景的边界点.两个层次(影片和镜头)信息的充分利用使得方法简单易操作,基于压缩视频的处理方法提高了运算速度,实验结果表明了该检测方法的有效性.     

新闻视频单元高效切分方法的研究与实现

提出了一个基于口播检测的高效新闻视频单元切分方法。该方法首先检测出新闻视频的镜头边界;然后从每个镜头中提取出关键帧,并计算出关键帧的直方图和SIFT特征;最后通过关键帧聚类获取新闻视频中的所有口播镜头,并以此为依据将新闻视频分割成多个语义单元。基于以上方法,开发了用于新闻视频单元切分的软件系统。该系统能够准确、高效地实现新闻单元的自动切分,有效地减轻视频切分时的工作强度,满足新媒体时代节目快速制作的要求。     

Compressed video processing for cut detection

One of the challenging problems in video databases is the organisation of video information. Segmenting a video into a number of clips and characterising each clip has been suggested as one mechanism for organising video information. This approach requires a suitable method to automatically locate cut points (boundaries between consecutive camera shots in a video). Several existing techniques solve this problem using uncompressed video. Since video is increasingly being captured, moved, and stored in compressed form, there is a need for detecting shot boundaries directly in compressed video. The authors address this issue and show certain feature extraction steps in MPEG compressed video that allow the implementation of most of the existing cut detection methods developed for uncompressed video for MPEG video stream. They also examine the performance of three tests for cut detection by viewing the problem of cut detection as a statistical hypothesis testing problem. As the experimental results indicate, the statistical hypothesis testing approach permits fast and accurate detection of video cuts     

一种快速有效的视频镜头边界检测方法

本文在分析现有的镜头边界检测方法的基础上,提出了一种层次化镜头边界检测方法.首先我们利用小波变换对信号奇异点的检测能力和对噪声具有较强的鲁棒性,实现了镜头边界的预检测.然后我们通过对候选镜头边界的虚警分析,有效地降低了闪光灯和快速运动对镜头边界检测产生的影响,并实现了镜头突变检测.在渐变类型分类中,我们分别实现了淡入淡出、扫换和溶解等三种重要镜头边界类型的检测.实验结果表明,本文方法能够快速、有效地实现视频镜头边界类型的检测,对运动和光线变化有较强的鲁棒性.     

基于模糊粗糙集的新闻视频镜头边界检测方法

镜头边界检测是实现基于内容的视频检索的一个重要步骤.为了将视频分割成镜头,现有的方法大都是首先提取大量的特征然后构造相异性测度函数.然而,太多的特征就会降低算法的效率.因此,有必要对镜头边界检测的规则进行特征约简.本文将粗糙集中的属性重要性和模糊粗糙集中的分类精度相结合定义了模糊粗糙算子,并构造了相异度检测函数.最后给出了镜头边界检测的一般性规则.由于本文检测方案的自适应性,因此适合于各种类型的新闻视频.用来自中央电视台的3个多小时的新闻视频所做的镜头边界检测实验获得了95.4%的查全率和96.1%的准确率.     

一种基于模糊逻辑的MPEG压缩视频场景转换检测方法

镜头边界的自动检测是实现基于内容的视频检索必不可少的第一步,目前大多数的场景转换检测方法都是基于非压缩视频的,而越来越多的视频数据却以压缩形式存在。本主文提出了一咱新的针对ＭＰＥＧ压缩视频的场景转换检测算法,它利用ＤＣ序列和运动向量计算像素差、直方图差、统计差和具有“真实”运动向量的宏块所占的比例,然后用模糊逻辑对上述参量加以综合隶属度用自适应的方法确定。实验表明这种镜头检测算法具有较高的检出率和     

级联手工特征与深度特征的视频关键帧检测方法

关键帧检测是有效的视频内容分析的关键环节。常用的基于手工特征的方法运行效率高但很难有效表征关键帧特征,因而性能不好。基于深度特征的方法因为网络结构复杂,导致效率不高。在体育比赛类视频中,关键帧常为比赛转播中镜头变化的最后一帧。但广播视频中除了包含比赛视频还包括很多其他类型的镜头如中场休息、渐变镜头等。因此检测最后一帧包含很多比赛无关内容。针对这一问题,本文提出了一种手工特征与深度特征相结合的视频关键帧检测方法。首先基于颜色直方图特征进行镜头边界检测获取最后一帧。进一步基于直方图相似性提出一种类似聚类的方法得到候选关键帧。最后,基于深度神经网络对候选关键帧进行分类,得到真正的关键帧。在冰壶比赛视频和篮球比赛视频上的对比实验结果表明,相对于传统的背景差分法、光流法等,本文提出方法能够快速、可靠地提取关键帧。      

一种基于交互信息量的视频摘要生成方法

提出了一种基于交互信息量的视频摘要生成方法。该方法首先使用基于交互信息量的方法进行视频镜头检测,通过对检测到镜头帧的聚类提取镜头候选关键帧。然后对候选关键帧按照相邻帧间交互信息量的比较来提取镜头关键帧,最后将镜头关键帧按时序排列在一起形成视频摘要。试验表明,这种关键帧提取算法是有效的,其建立的视频摘要能较好的反映原视频的内容。     

Advanced news video parsing via visual characteristics of anchorperson scenes

In this paper, we present an advanced news video parsing system via exploring the visual characteristics of anchorperson scenes, which aims to provide personalized news services over Internet or mobile platforms. As the anchorperson shots serve as the root shots for constructing news video, the addressed system firstly performs anchorperson detection which divides the news into several segments. Due to the manipulation of multi-features and post-processing, our method of anchorperson detection can even be efficiently applied to news video whose anchorperson scenes are most challenging and complicated. Usually, the segments produced from anchorperson detection are regarded as news stories. However, an observation in our database proves this is not true because of the existing of interview scenes. These interview scenes are showed in the form that interviewer (anchorperson) and interviewee recursively appear. Thus, a technique called interview clustering based on face similarity is carried out to merge these interview segments. Another novel aspect of our system is entity summarization of interview scenes. We adopt it in the system at final. The effectiveness and robustness of the proposed system are demonstrated by the evaluation on 19 hours of news programs from 6 different TV Channels.     

Graph-based video sequence matching using dominant colour graph profile (DCGP)

This paper presents a fast and effective technique for videos’ visual similarity detection and measurement using compact fixed-length signatures. The proposed technique (dominant colour graph profile DCGP) extracts and encodes the spatio-temporal information of a given video shot into a graph-based structure (tree) that fully captures this vital information. The graph structured properties are utilized to construct a fixed-length video signature of 112 decimal values per video shot. The encoded spatio-temporal information is extracted following channelling each video frame into a block-based structure, where the positions of respective blocks are tracked across video frames and encoded into multiple DCGP trees. The proposed technique provides a high matching speed (>2000 fps) and robust retrieval performance. The experiments on various standard and challenging datasets shows the framework’s robust performance, in terms of both, retrieval and computational performances.     

High accuracy flashlight scene determination for shot boundary detection

Shot boundary detection, or scene change detection, is a technique used in the initial phase of video indexing. One of the problems in the detection is the discrimination of abrupt scene change from flashlight scenes. The usual discriminate method tests the similarity of the frame before and after a suspected flashlight effect. However, the performance of such a technique in discriminating flashlight scene from abrupt scene change can be affected by the scene content. To overcome this, we present a novel method that utilises the edge direction, thereby reducing erroneous matching with increasing dilation radius. This improves the accuracy of similarity testing and reduces the amount of erroneously matched edges by four times. Our experiment in discriminating flashlight effect from abrupt scene change frame pairs shows that our technique produces a perfect detection, which cannot be achieved by normal edge-based detection. Such a contribution is important as it improves the indexing of real life video.     

Shot boundary detection in the presence of illumination and motion

Detection of gradual transition and the elimination of disturbances caused by illumination change or fast object and camera motion are the major challenges to the current shot boundary detection techniques. These disturbances are often mistaken as shot boundaries. Therefore, it is a challenging task to develop a method that is not only insensitive to various disturbances but also sensitive enough to capture a shot change. To address these challenges, we propose an algorithm for shot boundary detection in the presence of illumination change, fast object motion, and fast camera motion. This is important for accurate and robust detection of shot boundaries and in turn critical for high-level content-based analysis of video. First, the propose algorithm extracts structure features from each video frame by using dual-tree complex wavelet transform. Then, spatial domain structure similarity is computed between adjacent frames. The declaration of shot boundaries are decided based on carefully chosen thresholds. Experimental study is performed on a number of videos that include significant illumination change and fast motion of camera and objects. The performance comparison of the proposed algorithm with other existing techniques validates its effectiveness in terms of better Recall, Precision, and F1 score.     

Efficient Filtering and Clustering Methods for Temporal Video Segmentation and Visual Summarization

Automatic temporal segmentation and visual summary generation methods that require minimal user interaction are key requirements in video information management systems. Clustering presents an ideal method for achieving these goals, as it allows direct integration of multiple information sources. This paper proposes a clustering-based framework to achieve these tasks automatically and with a minimum of user-defined parameters. The use of multiple frame difference features and short-time techniques are presented for efficient detection of cut-type shot boundaries. Generic temporal filtering methods are used to process the signals used in shot boundary detection, resulting in better suppression of false alarms. Clustering is also extended to the key frame extraction problem: Color-based shot representations are provided by average and intersection histograms, which are then used in a clustering scheme to identify reference key frames within each slot. The technique achieves good compaction with a minimum number of visually nonredundant key frames.     

基于窗帧差的镜头边界系数模型及其应用

针对视频结构分析提出了一种新的镜头边界系数模型.首先,按照特定的计算方法计算当前帧的窗帧差,使得窗帧差在镜头内与镜头边界呈现不同的特征;然后,采用相应的镜头边界检测算子计算镜头边界相似系数,镜头边界相似系数越大,则该帧越可能是镜头边界;最后,为了提高镜头边界相似系数的检测能力,根据镜头边界相似系数定义了镜头边界系数.镜头边界系数具有良好的特性,可以单独检测镜头边界,也可以与传统的镜头边界检测方法相结合,为镜头边界检测提供了一种鲁棒的框架.实验结果表明,基于镜头边界系数模型的镜头边界检测方法能明显改善镜头边界检测结果.     

一种自动选取阈值的视频镜头边界检测算法

镜头边界检测是实现基于内容的视频检索的一个重要步骤.文中介绍了现有的镜头边界检测的基本方法,并针对其不足提出了一种自动选取阈值的、综合考虑颜色和空间特征的镜头边界检测算法.该方法能较好地检测出镜头突变和物体运动以及光线变化等情况,对渐变镜头也能达到检测的目的.实验结果表明算法能够有效地检测出视频镜头边界.