基于信息熵的自适应阈值视频镜头检测方法

文中提出了一种基于信息熵的自适应阈值视频镜头检测方法。首先,利用小波变换提取图像的颜色特征和纹理特征,然后利用信息熵方法来实现对突变和渐变镜头边界的检测,并根据滑动窗口中差值的分布来动态计算局部阈值,提高了镜头边界检测算法的精度。该方法能较好地检测出镜头突变,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。     

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.     

基于聚类的镜头边界检测算法

镜头边界检测是基于内容视频检索的重要组成部分。为从不同类型的视频中有效地检测出视频镜头边界,提出一种视频镜头边界检测算法。通过视频帧图像的颜色特征,得到视频的相似性矩阵,根据突变镜头和渐变镜头在Affinity Propagation聚类结果中的不同特点,运用双阈值法检测镜头边界。实验结果表明,该算法从视频的本身信息分布出发,能自动快速地检测出镜头边界。     

基于聚类的镜头边界检测算法

镜头边界检测是基于内容视频检索的重要组成部分。为从不同类型的视频中有效地检测出视频镜头边界,提出一种视频镜头边界检测算法。通过视频帧图像的颜色特征,得到视频的相似性矩阵,根据突变镜头和渐变镜头在Affinity Propagation聚类结果中的不同特点,运用双阈值法检测镜头边界。实验结果表明,该算法从视频的本身信息分布出发,能自动快速地检测出镜头边界。     

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。     

基于颜色空间的自适应阈值镜头分割算法

镜头分割是基于内容的视频检索的关键步骤,它会直接影响到视频检索的效果。文中介绍了几种常用的镜头分割方法,并且根据YUV颜色空间的各分量可分离的特点,提出了一种改进的基于YUV颜色空间的自适应阈值镜头分割方法。在突变镜头检测模块,为了消除由噪声带来的误检,加入了帧差值比法。在渐变镜头检测模块,使用了滑动窗口值方法。镜头分割的难点是渐变检测,算法在渐变检测上也取得了不错的成果。经过大量实验结果表明,改进的算法对镜头分割有很好的实验效果,算法计算复杂度低,易于实现。     

基于颜色直方图变化率的视频镜头检测

镜头分割是基于内容的视频检索(CBVR)的关键步骤,它会直接影响到视频检索的结果。针对传统镜头分割算法实现过程复杂,渐变检测不理想等不足,提出了颜色直方图变化率HCR(Histogram Change Ratio)的概念,并在此基础上提出了一种基于颜色直方图变换率的视频镜头分割算法。实验证明,该镜头分割算法实现过程简单,而且对镜头突变和渐变均具有很好的检测效果。     

一种基于双阈值法改进的镜头边界检测算法

提出了一种基于双阈值法改进的镜头边界检测算法.该方法的优势在于通过复检有效降低了突变镜头以及淡入淡出渐变镜头的误检率,同时该方法针对包含较多视频帧的镜头给出了一种进行溶解型渐变镜头检测的方法,有效地提高了溶解型渐变镜头的检出率.实验表明与原算法相比其查全率约提高了11%.     

一种有效的视频镜头检测方法

镜头是视频数据的基本单位,对视频镜头的检测是视频检索和浏览技术的基础工作。本文提出了一种简单的镜头检测方法,该方法首先使用视频亮度信息检测突变镜头,然后使用亮度和色度信息检测渐变镜头。通过实验与典型的“双阈值”镜头检测法相比较,证明该算法是有效的。     

一种新的视频镜头分割算法

镜头是视频分析和索引的基础，但是自动的镜头分割，尤其是渐变切换的检测还是一个很有挑战性的课题。本文提出了一种利用直方图与模板匹配相结合来进行视频镜头切变检测的算法和一种利用图象灰度级平均(MGL)来进行渐变检测的算法，该渐变检测算法能有效区分摄象机镜头的运动和渐变。实际测试证明，利用本文算法进行视频镜头检测
能取得比较好的效果。     

基于模式模板的MPEG压缩视频序列的划变转换检测

为了在进行视频分段时，对一些复杂的和奇异的Wipe转换模式进行检测，得出了一种基于模式模板的MPEG压缩视频序列的划变转换检测方法。该方法是在刻画划变转换时，用一种视频帧间图象特征变化信息的模式模板；并用一个基于该模式模板的算法，来检测包括各种奇异转换在内的划变转换，同时为了消除运动干扰，还引入了Hough变换。实验结果显示，该方法能精确检测划变转换区的位置和识别相应的转换模式，另外，无论是已存在的或将来出现的划变转换模式，该算法都同样适用。     

基于DC系数和运动矢量的快速场景分割算法

场景分割技术是动态视频分析和基于内容的视频检索的基础，以检测出来的场景作为基本单元，可以进一步对视频内容进行分析和建立索引．本文旨在提出一种基于MPEG压缩视频流的场景分割算法，利用MPEG数据流中已有的DCT DC系数和运动矢量，来检测场景的变换，从而实现场景分割，针对实际视频流中场景突变和渐变两类变换．本文提出两种方法分别处理不同情况，对于突变检测，该算法可以定位到帧，由于该算法进行最小程度的解码，降低了计算复杂度，因而大大提高了检测速度．     

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.     

一种分层的和多分辨的镜头边界检测方法

提出了一种分层的和多分辨的镜头边界检测方法。该方法对各种不同的镜头间过渡类型给出了用不同方法进行联合检测的方案,该方案主要分为突变镜头检测（即视频切分）,淡化过渡检测、溶解过渡检测及划变过渡检测4个部分。检测方案并不是简单地将各种方法拼接在一起,而是通过小波变换的多分辨分析将它们有机地结合起来,相互关联,达到有效检测结果。首先用FCM聚类算法进行视频切分,然后根据聚类结果分别在整数小波分解后的高频部分用Gaussian加权Hausdorff距离结合边界改变率算法检测淡化过渡;对分解后的低频部分用所提出的SCD算法（Similarity of color distribution based method）检测溶解过渡,并通过自适应调节权系数（系数盲调节）使检测相异度函数更能适用于多种视频片段。最后根据切分以及前面两种过渡检测的结果,利用三维小波分解后高频成分中的运动部分所定义的运动矢量来检测划变过渡。用实际视频数据所做的仿真实验结果表明,该方法不但能同时检测突变过渡和渐变过渡,而且能准确地判断渐变过渡的类型及其位置。此外,还能有效地抑制闪光、运动等的影响,从而提高了检测精度。     

Shot Change Detection Using Scene-Based Constraint

A key step for managing a large video database is to partition the video sequences into shots. Past approaches to this problem tend to confuse gradual shot changes with changes caused by smooth camera motions. This is in part due to the fact that camera motion has not been dealt with in a more fundamental way. We propose an approach that is based on a physical constraint used in optical flow analysis, namely, the total brightness of a scene point across two frames should remain constant if the change across two frames is a result of smooth camera motion. Since the brightness constraint would be violated across a shot change, the detection can be based on detecting the violation of this constraint. It is robust because it uses only the qualitative aspect of the brightness constraint—detecting a scene change rather than estimating the scene itself. Moreover, by tapping on the significant know-how in using this constraint, the algorithm's robustness is further enhanced. Experimental results are presented to demonstrate the performance of various algorithms. It was shown that our algorithm is less likely to interpret gradual camera motions as shot changes, resulting in a significantly better precision performance than most other algorithms.     

一种用于自动视频分段的WIPE转换检测和模式识别方法

自动视频分段是视频分类、组织和索引的基本步骤 ,其目标是检测和识别出各种镜头转换边界 .划变(Wipe)被认为是其中最难以检测和识别的镜头转换模式 .提出了一种新的基于模式模板的 Wipe转换区检测和模式识别方法 ,在该方法中 ,首先提出了一种模式模板的概念用于对各种 Wipe转换建立模式模板库 ,然后在模式模板库的基础上设计了一个通用的匹配算法 ,最后 ,通过对匹配结果进行 Hough变换 ,使得在存在相当大的运动干扰情况下 ,仍能精确检测转换区位置和识别转换模式 ,具有很好的鲁棒性 .该方法解决了对目前存在的各种复杂和奇异 Wipe转换模式的检测和识别问题 ,对以后出现的 Wipe转换模式同样适用 ,为此类问题的解决提供了一种新的思路     

基于自适应GOP的三维提升小波视频编码技术

在三维提升视频编码框架中,视频运动场景切换时相邻视频帧之间的时间相关性将显著减弱,使得解码视频图像在场景切换处质量急剧下降.针对这一问题,提出了一种新的基于视频亮度分量的场景切换检测方法,并根据场景切换自适应分配图像组(groupof picture GOP)大小.实验结果表明,该自适应分配GOP策略有效提高了三维提升小波视频图像的编解码质量,降低了场景切换对视频编码的影响.     

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.     

基于预处理的视频镜头边界检测算法

针对视频镜头边界检测的高时耗问题,提出了一种基于视频预处理的视频镜头边界检测(SBD)改进算法。通过使用自适应的阈值选择可能包含镜头边界的候选段,候选段内首帧与其余各帧进行相似度对比检测出镜头起始帧,并立即检测切变。若候选段中不包含切变,则进行渐变检测。调整候选段以保证镜头边界位于同一段内,段内其余各帧与起始帧进行相似度对比确定镜头结束帧。实验结果表明,所提算法镜头边界识别准确率能够达到90%以上,且与倒三角模式匹配方法相比能够节约时间15.6%~30.2%;与对渐变和切变分别检测的算法相比,该算法能够在满足识别率的基础上提升检测速度。     

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.