一种基于小波变换的视频对象分割算法

新一代视频编码标准MPEG－4提出了基于内容的视频编码的概念，它充分考虑到视频场景的特点，对不同对象用不同方法进行编码，这样视频对象分割就成为该标准的关键技术之一。该文提出一种利用小波变换分割视频序列中运动对象的算法。算法的主要思想是通过对视频序列在时间轴上作小波变换提取视频对象的运动信息进行视频对象的分割。实验结果证明，本算法能比较准确地分割出运动视频对象。     

一种从MPEG视频流中快速分割镜头的算法

镜头分割算法是实现视频检索的关键，本文提出一种针对MPEG视频流的快速分割镜头的算法，介绍了该算法的基本原理与实现过程。该算法利用压缩视频中的可用信息，通过计算帧的内编码宏块数量的比例作为是否需要进行镜头分割判别的标识，进而计算MPEG视频流B帧各宏块编码类型来检测发生镜头分割的帧住置，并进行了相关的实验。实验
结果表明，该算法能快速有效地检测镜头分割，且计算量较小。     

基于视频页的视频流分割方法

提出了基于视频页的视频流分割方法。该方法通过定义视频帧的色彩相近区、光照变化区和色彩变化区,获得视觉内容改变的局部区域,由此进一步确定两帧的视觉内容相似性,从而实现视频页的分割,经与几种有代表性的视频流分割算法的实验比较,证明该检测算法不仅对光照变化的影响具有很高的鲁棒性,而且使得分割能在较高信噪比上进行,明显提高了视频流分割的准确性。     

电视节目自动分割算法

近年来，视频内容的结构化分析逐渐成为一个研究热点．传统意义上认为场景是最高层次的视频处理单元．然而随着研究工作的不断深入，需要提出一种更高层次的视频处理单元——节目(program)．通过节目管理大量存在的场景，形成更丰富的层次；另外，节目还可以作为视频节目分类算法的输入，从而实现从分割到分类的全部自动化．在传统层次结构的基础上，进一步拓宽视频处理单元的概念，在场景层与视频层之间增加节目层．并且提出一种针对特定电视频道的视频流的节目自动分割算法，实验结果表明该算法具有较好的性能．     

在MPEG视频上的语义视频对象分割改进算法

随着MPEG－4，MPEG－7等标准的提出，如何提取语义视频对象已成为当前视频压缩和检索领域共同的研究课题。特别是MPEG－7对视频对象的形状检索提出了具体要求。针对顾创等人的WaterShed算法不能很好处理现有MPEG－1视频数据的缺陷，提出改进算法，从而能够在MPEG视频流中提取到具有光滑轮廓的语义视频对象，算法主要有以下两点改进：提出将类成员数作为分类算法的参数；有压缩域和解码后的图像上先后进行两次分割。实验结果表明，改进算法在鲁棒性和分割结果精度上比原算法有较大的改进，其分割结果可用于轮廓检索和重要视频对象检索。     

一种带验证的自适应镜头分割算法

文中针对镜头分割在基于内容的视频检索中的重要性,首先介绍了普通的镜头分割方法,进而针对这些方法的不足,提出了一种带检测的自适应镜头分割算法,该方法能够检测中视频中的突变,渐变等镜头变换,通过实际测试,结果表明算法能够取得较高的正确率.     

基于宏块类型信息的自适应场景变换检测算法

提出了一种压缩域中利用B帧的宏块类型信息自适应场景变换检测算法，采取滑窗方式自适应地检测场景突变和场景渐变的准确位置。利用电影、动画、新闻等素材作为实验视频库，并利用MPEG 2作为实验平台，取得了较高的查全率和查准率。     

一种基于运动特征的快速镜头边界检测方法

提出了一种基于MPEG视频流运动特征的镜头边界检测模糊推理方法。首先提取MPEG视频流中每帧的宏块信息，然后分析这些信息分别得到相似度、相似度差、运动活动性强度差、运动集中度差等，并将它们作为隶属度函数的输入量，按照一定的推理规则得到突变、渐变、无镜头变换三种情况的隶属度值，值最大者决定该帧是何种类型。实验结果表明该方法具有较高的检测精度，而且由于该方法不用对MPEG视频流进行解压缩，因此处理速度快，适合于实时应用场合。     

基于压缩域的关键帧快速提取方法

关键帧提取技术是基于内容检索和视频分析的基础。关键帧的使用减少了视频索引的数据量，同时也为视频摘要和检索提供了一个组织框架。首先介绍了目前的关键帧提取技术，然后提出了一种基于运动特征利用模糊推理算法从MPEG视频流中提取关键帧的方法。由于处理过程是直接从MPEG的压缩视频提取，不需对其解压，所以计算复杂度低，提高了提取速度。实验证明该方法效率高，可以比较好地代表视频内容。     

基于决策树的MPEG视频镜头分割算法

压缩视频镜头的分割是视频内容分析中的一个难点,由于镜头在组织和索引视频中起关键性的作用,提出了一种基于决策树的MPEG视频镜头分割算法。该算法采用决策树这种机器学习方法对样本视频进行训练,通过融合运动信息、颜色、边缘等特征获得镜头分割的最佳阈值,较好地解决了压缩视频处理中检测镜头突变和渐变难题,同时还能够检测出镜头是否产生闪光现象和相机运动的产生。实验证明本算法在压缩视频镜头检测方面取得了较好的检测结果。     

一种高效的视频切变检测算法

介绍了一种可以在压缩域和非压缩域实时检测视频切变的算法。算法采用了大小双重窗口,利用大窗口全局阈值提取候选切变位置,再在以候选切变位置为中心的小窗口中结合双侧和单侧判断进一步检测真实切变位置。算法能有效地避免因摄像机和目标的剧烈运动造成造成误检和漏检的情况,在检测五段视频的实验中,取得１００％的查全率和９６％的准确率。     

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. A novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.     

Sudden scene change detection in compressed video using interpolated macroblocks in B-frames

This paper addresses an important area in video processing, namely compressed domain processing. For video indexing, video scene transition detection is an essential step to segment the video. Current techniques for scene change detection tend to suffer from a major limitation as most of them cannot identify scene transitions in the compressed domain. Since most video is expected to be stored in the compressed domain, scene transition detection in this domain is highly desirable. In this paper an algorithm for video scene change detection is proposed to overcome this limitation. In this scheme, properties of the B-frames are used as it is capable of measuring the correlation between two adjacent reference frames. The results show that this scheme performs better than schemes based on P-frames. Proposed scheme can be directly applied with compressed data with minimum decompression and hence it is computationally efficient and makes real time implementations possible. Results show that video scene transitions can be identified satisfactorily with the proposed scheme.     

基于内容的视频检索的突变场景变换探测算法

讲述了目前正在使用的突变场景变换探测算法，并列举了一种新的基于压缩视频的突变场景变换探测算法，实验结果显示，这个算法不受视频种类的限制，能取得满意的结果。     

一种基于模型的扫换检测方法

视频自动分割是实现视频数据库检索必不可少的一个过程,其基础是镜头边界检测.当前已有的算法能够较准确地检测出镜头突变,但对于镜头的渐变则常常会漏检,这是由于镜头渐变时帧间差没有一个明显的峰值,因而其检测比突变检测要困难得多.扫换是一种常用的视频空间编辑手段,用于实现多种镜头变化.通过分析各种类型的扫换,提出了一种新的基于视频空间编辑模型的扫换检测算法,其性能优于Alattar提出的基于统计特征的算法.对用AdobePremiere5.1生成的各种扫换视频进行检测.实验结果表明,这种扫换检测算法能够较好地适应     

Fast scene change detection using direct feature extraction fromMPEG compressed videos

In order to process video data efficiently, a video segmentation technique through scene change detection must be required. This is a fundamental operation used in many digital video applications such as digital libraries, video on demand (VOD), etc. Many of these advanced video applications require manipulations of compressed video signals. So, the scene change detection process is achieved by analyzing the video directly in the compressed domain, thereby avoiding the overhead of decompressing video into individual frames in the pixel domain. In this paper, we propose a fast scene change detection algorithm using direct feature extraction from MPEG compressed videos, and evaluate this technique using sample video data, First, we derive binary edge maps from the AC coefficients in blocks which were discrete cosine transformed. Second, we measure edge orientation, strength and offset using correlation between the AC coefficients in the derived binary edge maps. Finally, we match two consecutive frames using these two features (edge orientation and strength). This process was made possible by a new mathematical formulation for deriving the edge information directly from the discrete cosine transform (DCT) coefficients. We have shown that the proposed algorithm is faster or more accurate than the previously known scene change detection algorithms     

Effective wipe detection in MPEG compressed video using macro block type information

For video scene analysis, the wipe transition is considered most complex and difficult to detect. In this paper, an effective wipe detection method is proposed using the macroblock (MB) information of the MPEG compressed video. By analyzing the prediction directions of B frames, which are revealed in the MB types, the scene change region of each frame can be found. Once the accumulation of the scene change regions covers most of the area of the frame, the sequence will be considered a motionless wipe transition sequence. Besides, uncommon intracoded MB of the B frame can also be applied as an indicator of the motion wipe transition. A very simple analysis based on small amount of MB type information is sufficient to achieve wipe detection directly on MPEG compressed video. Easy extraction of MB type information, low-complexity analysis algorithm and robustness to arbitrary shape and direction of wipe transitions are the great advantages of the proposed method.     

Video scene analysis in 3D wavelet transform domain

This paper proposes a novel scene analysis algorithm based on three-dimensional discrete wavelet transform (3D DWT). Based on the correlation among the adjacent frames, video frames can be considered as four categories: abrupt scene transition, motion scene, gradual scene transition and static scene, which are ranked from low to high according to the strength of the correlation. Through the investigation of the particular temporal and spatial distribution of each category, the correlation among adjacent frames could be described by the 3D DWT coefficients related statistical features, which are the energy of high-frequency coefficients difference, the sum of high-frequency coefficients magnitudes and the difference of low-frequency coefficients magnitudes. The energy of high-frequency coefficients difference is first used to detect the abrupt scene transition including cut and flashlight. Then all the three features are input to SVM for the purpose of analyzing the residual scenes and detecting the gradual scene transition, such as dissolve and fade. Experimental results show the method to be effective not only for the abrupt scene transition detection, but also for the gradual scene transition detection.     

AVCD-FRA: A novel solution to automatic video cut detection using fuzzy-rule-based approach

Video shot boundary detection (SBD) is a fundamental step in automatic video content analysis toward video indexing, summarization and retrieval. Despite the beneficial previous works in the literature, reliable detection of video shots is still a challenging issue with many unsolved problems. In this paper, we focus on the problem of hard cut detection and propose an automatic algorithm in order to accurately determine abrupt transitions from video. We suggest a fuzzy rule-based scene cut identification approach in which a set of fuzzy rules are evaluated to detect cuts. The main advantage of the proposed method is that, we incorporate spatial and temporal features to describe video frames, and model cut situations according to temporal dependency of video frames as a set of fuzzy rules. Also, while existing cut detection algorithms are mainly threshold dependent; our method identifies cut transitions using a fuzzy logic which is more flexible. The proposed algorithm is evaluated on a variety of video sequences from different genres. Experimental results, in comparison with the most standard cut detection algorithms confirm our method is more robust to object and camera movements as well as illumination changes.