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。     

H．264压缩域中利用Biased-SVM检测镜头边界

为了直接从H．264码流中检测镜头边界,提出了利用H．264压缩域多特征和Biased—SVM（不平衡支持向量机）分类算法的检测方法。分析帧类型、宏块类型、运动矢量、帧内预测模式等信息,以获得发生镜头突变和渐变的特征。针对镜头边界帧的数量远少于视频帧总数的特点,用Biased—SVM分类方法将视频帧分为突变帧、渐变帧和非镜头边界帧。在TRECVID视频集上的实验结果表明,与其他H．264压缩域的算法相比,该算法有更好的性能。     

Fast video shot boundary detection framework employing pre-processing techniques

Video shot boundary detection is the initial and fundamental step towards video indexing, browsing and retrieval. Great efforts have been paid on developing accurate shot boundary detection algorithms. However, the high computational cost in shot detection becomes a bottleneck for real-time applications. The problem of making a balance between detection accuracy and speed is addressed in this paper, and a novel fast detection framework is presented. The general framework that employs pre-processing techniques can improve both detection speed and precision. In the pre-processing stage, adaptive local thresholding is adopted to classify non-boundary segments and candidate segments that may contain shot boundaries. The candidate segments are refined using bisection-based comparisons to eliminate non-boundary frames. Only refined candidate segments are preserved for further detections; hence, the speed of shot detection is improved by reducing detection scope. Moreover, prior knowledge about each possible shot boundary such as its type and duration can be obtained in the pre-processing stage, which can accelerate the consequent hard cut and gradual transition detections. Experimental results indicate that the proposed framework is effective in accelerating the shot detection process, and it can also achieve excellent detection accuracies.     

Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine

The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.     

Automatic video temporal segmentation based on multiple features

This paper investigates automatic video temporal segmentation techniques, also named shot boundary detection (SBD) techniques. Firstly, the existing SBD algorithms are reviewed in detail. Then, a new SBD algorithm is proposed aiming to obtain fast and accurate detection, and its performances are evaluated and compared with existing works. This algorithm computes the frame difference/similarity by such simple features as pixel difference and histogram difference, adopts motion-based difference to resist camera or object movements in the same shot and uses the flash detection to avoid false positives caused by light changes or flashes. The adopted features are computational efficient, and the combination of various features improve the detection accuracy. These properties make the algorithm suitable for real-time applications, such as broadcasted news segmentation.     

A vague set approach for identifying shot transition in videos using multiple feature amalgamation

Shot boundary detection (SBD) is the preliminary and most significant step in Content Based Video Retrieval (CBVR). As such the effectiveness of a CBVR system depends heavily on reliable detection of shot boundaries. In this work, a simple yet effective technique for amalgamating several distance features extracted from video frames has been proposed. The aim here is to develop a technique which is able to produce a better distance feature from the existing ones by hybridizing several distance metrics. In the proposed model, any number of distance features can be incorporated and fused together. The resultant feature is not only more robust but also immune to features which are inefficient. Robustness of the proposed method is tested by combining several low performing features with the more efficient ones. Several statistical amalgamation functions are also tested for determining the most efficient one in terms of F1 score. The power of vague sets has been harnessed to detect the shot boundaries effectively using the resultant distance feature. The proposed method is proved to be effective by means of the results obtained, which show that multiple feature amalgamation can lead to a hybrid distance feature which performs better than the best feature incorporated for SBD. The proposed technique is analyzed using ANOVA. A comparison with the other existing methods portray the efficacy of the proposed approach. This method can also be applied for other research problems where several features are to be fused together for producing superior results than the ones obtained by individual methods.     

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.     

Video shot boundary detection: Seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example.     

压缩域中叠化镜头的快速检测算法

随着网络和多媒体技术的不断发展，基于内容的多媒体信息检索技术变得越来越重要．同成熟的文本检索技术相比，视频检索还处在研究和探索阶段．视频检索的一个有效方法是将无结构的视频节目进行镜头分割，根据每个镜头的关键帧对视频建立索引．因此，镜头分割是基于内容的视频检索的基本步骤，在各种类型的镜头检测算法中，叠化镜头是很难检测的．根据叠化(dissolve)镜头内部预测帧预测误差能量和运动矢量分布特点，提出一种在压缩域中分割叠化镜头的新算法．与公开发表的同类算法相比，它具有以下优点：工作在压缩域上、速度快、鲁棒性好、精度更高．     

Metrics for shot boundary detection in digital video sequences

The detection of shot boundaries in video sequences is an important task for generating indexed video databases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to shot boundary detection. In addition, several standardized statistical tests that have not been applied to this problem, as well as three new metrics, are considered. A mathematical framework for quantitatively comparing metrics is supplied. Experimental results based on a video database containing 39,000 frames are included.     

Novel automatic video cut detection technique using Gabor filtering

Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many other digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. This paper approaches the video cut transition detection domain, providing a novel feature-based automatic identification method. We propose a feature extraction technique that uses 2D Gabor filtering, computing tridimensional image feature vectors for the video frames. Most shot cut detection techniques use a thresholding operation to discriminate between the inter-frame difference metric values and thus identify the video break points. Our identification approach is not threshold-based, using an automatic unsupervised distance classification procedure instead of a threshold. Thus, we provide a region-growing based classification approach, that proves to be very efficient in clustering the distances between feature vectors of consecutive frames. The two resulted distance classes determine a satisfactory video shot detection.     

一种快速的自适应镜头边界检测方法*

为减少视频处理数据量及提高检测效率,提出一种快速的自适应镜头检测方法。该方法利用跳帧法从原始视频帧中提取新的视频帧序列,通过计算滑窗内亮度直方图帧差到帧差均值的距离来检测镜头变换。实验结果表明,在显著提高检测效率的同时,该方法能有效检测出镜头变换。     

Video parsing and browsing using compressed data

Parsing video content is an important first step in the video indexing process. This paper presents algorithms to automate the video parsing task, including partitioning a source video into clips and classifying those clips according to camera operations, using compressed video data. We have developed two algorithms and a hybrid approach to partitioning video data compressed according to the JPEG and MPEG standards. The algorithms utilize both the video content encoded in DCT (Discrete Cosine Transform) coefficients and the motion vectors between frames. The hybrid approach integrates the two algorithms and incorporates multi-pass strategies and motion analyses to improve both accuracy and processing speed. Also, we present content-based video browsing tools which utilize the information, particularly about the shot boundaries and key frames, obtained from parsing.     

MPEG压缩域多尺度镜头切变检测算法

如何在压缩域进行镜头的切变检测一直是视频自动索引和检索中的难点。提出了一种MPEG压缩域多尺度镜头切变检测算法,在GOP、slot和B帧三个尺度上对MPEG视频流进行分析。通过对相邻I帧的检测,确定一个GOP中是否存在镜头切变;通过对slot的分析,确定镜头切变在GOP中所处的区域;通过对B帧的检测,确定镜头切变发生的确切位置。     

基于感知哈希和切块的视频相似度检测方法

长期以来视频侵权问题层出不穷,而检测视频相似度是视频侵权的重要手段。针对现有视频相似度检测方法中存在的多特征关系难以关联、时间复杂度高等问题,提出一种基于感知哈希和切块的快速对比方法。首先,利用视频的关键图像帧生成数字指纹集;然后,基于切块的方法构建相应的倒排索引,提高数字指纹间的对比速度;最后,根据得到的数字指纹间的汉明距离进行相似度判定。实验结果表明,与传统的感知哈希对比方法相比,该方法能在保证检测准确度的前提下将检测时间平均缩短93%;与多特征哈希（MTH）、自学习哈希（STH）、光哈希（SPH）等三种常见方法相比,所提方法的平均准确率均值（mAP）分别提高了1.4%、2%和2.3%,检测时间分别缩短了24%、32%和16%,验证了所提方法的可行性。     

足球视频镜头分类方法

根据视频语义分析和视频摘要等应用对于视频数据结构化的需求,提出了一种针对足球视频的镜头分类方法.通过logo模板匹配检测并定位出视频中的慢镜头,对其余的正常比赛部分做镜头边界检测完成视频切分.基于分块的思想,对正常比赛镜头帧计算其各块的场地像素比率值作为特征,利用SVM分类器将正常比赛镜头分为远镜头、中镜头、球员特写或场外镜头3类.至此,整个视频流可以表示为结构化的四类镜头类型标示序列.实验结果表明,该方法在视频切分和镜头类型识别的准确性方面具有良好的效果.     

基于局部多核支持向量机的视频镜头边界检测

提出了一种基于局部多核支持向量机的视频镜头边界检测方法.利用视频图像相邻帧的时空信息构建视频中间特征,在此基础上利用局部多核支持向量机将视频帧划分为边界帧和非边界帧.为了提高基于全局优化的多核支持向量机的检测精度,利用局部敏感哈希算法将视频帧投影全哈希子空间,结合多核学习方法为各个哈希子空间构建局部多核支持向量机,利用SMOTE上采样技术解决了视频图像边界帧和普通帧的不平衡问题.试验结果表明,本文提出的镜头边界检测方法的金全率和查准率得到了提高.     

基于视音频特征和文本信息的新闻视频自动场景分割

提出了一种基于视音频特征和文本信息的新的场景自动分割技术.其基本思想是先探测新闻视频的镜头边界,再用文本检测方法识别主题字幕帧以得到分割信息.用短时能量和短时平均零交叉率参数探测静音分片.将视音频特征和文本信息相结合以实现自动场景分割.实验使用135 400帧的测试数据达到了85.8%的准确率和97 5%的重现率.实验结果表明此方法是有效的、稳健的.     

结合标签传递的镜头边界检测与分类

镜头是视频的基本组成单元,其自动检测与分类是视频分析的重要任务。为了有效利用视频流视觉上的感知特性,提出一种基于标签传递的镜头边界检测与分类算法。该算法利用半监督学习的标签传递机制,通过视频流中连续多帧之间的相关性,将预先构造的初始状态标签通过相关图不断传递,以揭示不同镜头变化类型的视觉感知特征。然后利用多类SVM分类器进行镜头类型分类。实验结果表明,本文算法能有效识别多种镜头类型,对视频分析、检索等具有一定实用价值。