Detection and representation of scenes in videos

This paper presents a method to perform a high-level segmentation of videos into scenes. A scene can be defined as a subdivision of a play in which either the setting is fixed, or when it presents continuous action in one place. We exploit this fact and propose a novel approach for clustering shots into scenes by transforming this task into a graph partitioning problem. This is achieved by constructing a weighted undirected graph called a shot similarity graph (SSG), where each node represents a shot and the edges between the shots are weighted by their similarity based on color and motion information. The SSG is then split into subgraphs by applying the normalized cuts for graph partitioning. The partitions so obtained represent individual scenes in the video. When clustering the shots, we consider the global similarities of shots rather than the individual shot pairs. We also propose a method to describe the content of each scene by selecting one representative image from the video as a scene key-frame. Recently, DVDs have become available with a chapter selection option where each chapter is represented by one image. Our algorithm automates this objective which is useful for applications such as video-on-demand, digital libraries, and the Internet. Experiments are presented with promising results on several Hollywood movies and one sitcom.     

一种层次视频摘要生成方法

视频摘要是视频内容的一种压缩表示方式。为了能够更好地浏览视频，提出了一种根据浏览或检索的粒度不同来建立两种层次视频摘要(镜头级和场景级)的思想，并给出了一种视频摘要生成方法：首先用一种根据内容变化自动提取镜头内关键帧的方法来实现关键帧的提取；继而用一种改进的时间自适应算法通过镜头的组合来得到场景；最后在场景级用最小生成树方法提取代表帧。由于关键帧和代表帧分别代表了它们所在镜头和场景的主要内容，因此它们的序列就构成了视频总结。一些电影视频片段检验的实验结果表明，这种生成方法能够较好地提供粗细两种粒度的视频内容总结。     

Video segmentation using a histogram-based fuzzy c-means clustering algorithm

The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. Existing video segmentation methods can be classified into two groups: the shot change detection (SCD) approach for which thresholds have to be pre-assigned, and the clustering approach for which a prior knowledge of the number of clusters is required. In this paper, we propose a video segmentation method using a histogram-based fuzzy c-means (HBFCM) clustering algorithm. This algorithm is a hybrid of the two approaches aforementioned, and is designed to overcome the drawbacks of both approaches. The HBFCM clustering algorithm is composed of three phases: the feature extraction phase, the clustering phase, and the key-frame selection phase. In the first phase, differences between color histogram are extracted as features. In the second phase, the fuzzy c-means (FCM) is used to group features into three clusters: the shot change (SC) cluster, the suspected shot change (SSC) cluster, and the no shot change (NSC) cluster. In the last phase, shot change frames are identified from the SC and the SSC, and then used to segment video sequences into shots. Finally, key frames are selected from each shot. Simulation results indicate that the HBFCM clustering algorithm is robust and applicable to various types of video sequences.     

Video scene segmentation and semantic representation using a novel scheme

Grouping video content into semantic segments and classifying semantic scenes into different types are the crucial processes to content-based video organization, management and retrieval. In this paper, a novel approach to automatically segment scenes and semantically represent scenes is proposed. Firstly, video shots are detected using a rough-to-fine algorithm. Secondly, key-frames within each shot are selected adaptively with hybrid features, and redundant key-frames are removed by template matching. Thirdly, spatio-temporal coherent shots are clustered into the same scene based on the temporal constraint of video content and visual similarity between shot activities. Finally, under the full analysis of typical characters on continuously recorded videos, scene content is semantically represented to satisfy human demand on video retrieval. The proposed algorithm has been performed on various genres of films and TV program. Promising experimental results show that the proposed method makes sense to efficient retrieval of interesting video content.

Shot retrieval based on fuzzy evolutionary aiNet and hybrid features

As the multimedia data increasing exponentially, how to get the video data we need efficiently become so important and urgent. In this paper, a novel method for shot retrieval is proposed, which is based on fuzzy evolutionary aiNet and hybrid features. To begin with, the fuzzy evolutionary aiNet algorithm proposed in this paper is utilized to extract key-frames in a video sequence. Meanwhile, to represent a key-frame, hybrid features of color feature, texture feature and spatial structure feature are extracted. Then, the features of key-frames in the same shot are taken as an ensemble and mapped to high dimension space by non-linear mapping, and the result obeys Gaussian distribution. Finally, shot similarity is measured by the probabilistic distance between distributions of the key-frame feature ensembles for two shots, and similar shots are retrieved effectively by using this method. Experimental results show the validity of this proposed method.     

一种基于镜头聚类的视频场景分割方法

为了更好地进行视频信息检索和浏览,提出了一种利用视觉和运动特征来进行场景分割的方法,该方法在把镜头聚类到场景中时,不仅考虑同一场景内镜头的视觉特征相似性,而且还考虑了镜头的运动特征的一致性。此外,为避免过度分割,还提出了一种方法用来合并过度分割出的场景。实验结果表明,这种方法是有效的。     

基于时序结构图的视频流描述方法

通过对视频流的分解可以获得基于关键帧集的视频流表示,但这种表示方法不能反映出视频流中隐藏的故事发展关系,为揭示这种关系,提出了一种视频流的快速聚类算法,用于对视频流分解单元进行相关性分析,该算法通过检测视频镜头间的相似性和连续性,实现把来自同一摄像机的视频镜头归并入同一视频类,并帱此得到而且为矿山频流的快速浏览和检索提供了新的思路。     

基于卷积神经网络的多模态视频场景分割优化算法

针对基于内容的视频检索中场景分割效率有待提高的问题,提出了一种基于卷积神经网络提取特征的多模态视频场景分割优化算法。首先利用改进的VGG19网络从视频镜头中提取多种模态的底层特征和语义特征,再将这些特征组成向量,然后通过三重损失学习与镜头相似度计算等方法,使场景分割问题转换为对镜头边界的二分类问题,最后建立评分机制优化所得结果,获取分割好的视频场景及对应的场景边界,完成场景分割任务。实验结果表明,该算法能对视频场景进行有效分割,整体查全率与查准率分别能达到85.77%、87.01%。     

Shot clustering techniques for story browsing

Automatic video segmentation is the first and necessary step for organizing a long video file into several smaller units. The smallest basic unit is a shot. Relevant shots are typically grouped into a high-level unit called a scene. Each scene is part of a story. Browsing these scenes unfolds the entire story of a film, enabling users to locate their desired video segments quickly and efficiently. Existing scene definitions are rather broad, making it difficult to compare the performance of existing techniques and to develop a better one. This paper introduces a stricter scene definition for narrative films and presents ShotWeave, a novel technique for clustering relevant shots into a scene using the stricter definition. The crux of ShotWeave is its feature extraction and comparison. Visual features are extracted from selected regions of representative frames of shots. These regions capture essential information needed to maintain viewers' thought in the presence of shot breaks. The new feature comparison is developed based on common continuity-editing techniques used in film making. Experiments were performed on full-length films with a wide range of camera motions and a complex composition of shots. The experimental results show that ShotWeave outperforms two recent techniques utilizing global visual features in terms of segmentation accuracy and time.     

基于声像特征的场景检测 *

视频的结构分析是实现视频基于内容组织和检索的基础。目前 ,已经有很多用于视频镜头分割的成熟算法 ,但准确探测视频场景边界还比较困难。提出了一种融合视频中音频与可视特征进行场景检测的方法。该方法首先分别依据镜头的声、像特征相关性来对镜头进行聚类 ,然后综合处理依声、像相关性得到的镜头聚类来获取场景。实验结果证明 ,此方法较一般使用单一特征的场景检测方法提高了探测的准确率 ,同时也降低了误判率。     

Automatic scene detection for advanced story retrieval

Browsing video scenes is just the process to unfold the story scenarios of a long video archive, which can help users to locate their desired video segments quickly and efficiently. Automatic scene detection of a long video stream file is hence the first and crucial step toward a concise and comprehensive content-based representation for indexing, browsing and retrieval purposes. In this paper, we present a novel scene detection scheme for various video types. We first detect video shot using a coarse-to-fine algorithm. The key frames without useful information are detected and removed using template matching. Spatio-temporal coherent shots are then grouped into the same scene based on the temporal constraint of video content and visual similarity of shot activity. The proposed algorithm has been performed on various types of videos containing movie and TV program. Promising experimental results shows that the proposed method makes sense to efficient retrieval of video contents of interest.     

基于镜头的视频场景构造方法研究

由于内容颗粒度地小,镜头层次的检索不能满足视频内容使用的需要。场景比镜头高一个层次的视频内容结构单,能在一定程度上缓解镜头颗粒度过小的问题。“场景”是一组镜头的集合,在内容上包含相似的对象或包含类似的背景。本文提出了一种基于镜头构造频场景的思路,包括三个环节：镜头边界探测,镜头特征提取和镜头聚类。     

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.     

Movie scene segmentation using background information

Scene extraction is the first step toward semantic understanding of a video. It also provides improved browsing and retrieval facilities to users of video database. This paper presents an effective approach to movie scene extraction based on the analysis of background images. Our approach exploits the fact that shots belonging to one particular scene often have similar backgrounds. Although part of the video frame is covered by foreground objects, the background scene can still be reconstructed by a mosaic technique. The proposed scene extraction algorithm consists of two main components: determination of the shot similarity measure and a shot grouping process. In our approach, several low-level visual features are integrated to compute the similarity measure between two shots. On the other hand, the rules of film-making are used to guide the shot grouping process. Experimental results show that our approach is promising and outperforms some existing techniques.     

视频层次结构挖掘

视频处理的关键是视频信息的结构化,视频基本结构是由帧、镜头、场景和视频节目构成的层次结构。视频层次结构挖掘的一个简单框架是对视频进行镜头分割、抽取镜头特征和视频场景构造。论文在镜头分割的基础上提出了基于多特征的镜头聚类分析和基于镜头的场景边界检测两种视频场景构造方法,从而实现视频层次结构挖掘。实验表明,基于镜头的场景边界检测性能优于基于多特征的镜头聚类分析。     

一种改进的视频关键帧提取算法研究

视频镜头分割和关键帧提取是基于内容的视频检索的核心问题.提出了一种改进的关键帧提取算法,其为视频检索奠定了基础,镜头分割部分采用改进直方图方法及基于像素方法的综合方法.首先,通过结合直方图交集及非均匀分块加权的改进直方图方法,根据视频内容将视频分割为镜头;然后,利用基于像素的帧差法,对得到的检测镜头进行二次检测,优化检测结果;最后,在HSV颜色空间的基础上,计算每个镜头内每帧的图像熵,从而确定关键帧序列.实验结果表明,提出的改进算法所得到的关键帧结构紧凑且分布均匀.     

一种有效的视频场景检测方法

合理地组织视频数据对于基于内容的视频分析和应用有着重要的意义。现有的基于镜头的视频分析方法由于镜头信息粒度太小而不能反映视频语义上的联系，因此有必要将视频内容按照高层语义单元——场景进行组织。提出了一种快速有效的视频场景检测方法，根据电影编辑的原理，对视频场景内容的发展模式进行了分类，给出了场景构造的原则；提出一种新的基于滑动镜头窗的组合方法，将相似内容的镜头组织成为镜头类；定义了镜头类相关性函数来衡量镜头类之间的相关性并完成场景的生成。实验结果证明了该方法的快速有效性。     

一种基于语义的视频场景分割算法

针对如何在镜头基础上进行聚类，以得到更高层次的场景问题，提出了一个基于语义的场景分割算法。该算法首先将视频分割为镜头，并提取镜头的关键帧。然后计算关键帧的颜色直方图和MPEG-7边缘直方图，以形成关键帧的特征；接着利用镜头关键帧的颜色和纹理特征对支持向量机（SVM）进行训练来构造7个基于SVM对应不同语义概念的分类器，并利用它们对要进行场景分割的视频镜头关键帧进行分类，以得到关键帧的语义。并根据关键帧包含的语义概念形成了其语义概念矢量，最后根据语义概念矢量通过对镜头关键帧进行聚类来得到场景。另外．为提取场景关键帧，还构建了镜头选择函数，并根据该函数值的大小来选择场景的关键帧。实验结果表明，该场景分割算法与Hanjalic的方法相比，查准率和查全率分别提高了34．7％和9．1％。     

基于频繁镜头集合的视频场景分割方法

提出一种基于全局场景特征在视频序列中寻找频繁镜头集合,并通过局部语义特征精确定位视频场景边界的视频场景分割方法。首先对分析视频进行高精度镜头分割,选取具有代表性的镜头关键帧。然后提取各镜头关键帧的全局场景特征和局部特征,并利用局部特征聚类得到的视觉词对各个镜头关键帧进行语义标注。接下来计算基于全局场景特征的镜头间相关性,结合视频场景的概念和特性,在镜头关键帧序列中寻找局部频繁出现的相关性高的镜头集合,粗略定位视频场景位置。最后利用镜头关键帧的语义标注特征精确定位视频场景边界。实验证明该方法能够准确、有效地检测并定位到大部分视频场景。     

Real time repeated video sequence identification

In this paper, we present a real time system for detecting repeated video clips from a live video source such as news broadcasts. Our system utilizes customized temporal video segmentation techniques to automatically partition the digital video signal into semantically sensible shots and scenes. As each frame of the video source is processed, we extract auxiliary information to facilitate repeated sequence detection. When the video transition marking the end of the shot/scene is detected, we are able to rapidly locate all previous occurrences of the video clip. Our objective is to use repeated sequence information in our multimedia content analysis application to deduce semantic relationships among shots/scenes in the input video. Our real time video processing techniques are independent of source and domain and can be applied to other applications such as commercial detection and improved video compression.