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.     

A perceptual scheme for fully automatic video shot boundary detection

In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known “Information Seeking Mantra”. By adopting a top–down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user's “Region of Interest” or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.     

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.     

Tsallis entropy-based information measures for shot boundary detection and keyframe selection

Automatic shot boundary detection and keyframe selection constitute major goals in video processing. We propose two different information-theoretic approaches to detect the abrupt shot boundaries of a video sequence. These approaches are, respectively, based on two information measures, Tsallis mutual information and Jensen–Tsallis divergence, that are used to quantify the similarity between two frames. Both measures are also used to find out the most representative keyframe of each shot. The representativeness of a frame is basically given by its average similarity with respect to the other frames of the shot. Several experiments analyze the behavior of the proposed measures for different color spaces (RGB, HSV, and Lab), regular binnings, and entropic indices. In particular, the Tsallis mutual information for the HSV and Lab color spaces with only 8 regular bins for each color component and an entropic index between 1.5 and 1.8 substantially improve the performance of previously proposed methods based on mutual information and Jensen–Shannon divergence.     

基于拉格朗日高斯变换的奥运会视频分类机制研究

随着现代体育不断发展,奥运会承办比赛种类越 来越繁杂,对赛事视频分类提出了一 个新的挑战。现有的人工分类方法无法有效地区分团体竞技类比赛(球类)和个人竞技类比 赛(田径类)视频,从而进行大规模自动分类存储。然而,为了有效地重复使用这些视频文 件,需要对其进行分类存储,主要目的在于提高资源的利用率。针对人工分类手段太过于低 效的现状,本文对奥运会运动视屏内容分类问题进行研究,并提出了一种基于关键帧特征提 取和支持向量机(Supported Vector Machine,SVM)的视频分类方法。以第31届奥运会的 体育视频作为数据集,对每个视频进行关键帧提取和总结,并借由拉格朗日-高斯变换来计 算视频对应的特征向量,将特征向量作为SVM分类器的输入进行体育视频分类。实验结果表 明,对于任意奥运视频,提出的方法平均能够取得70%以上的正确分类率,而错误分类的比 例始终低于10%。特别地,对于奥运中的射击类视频,平均正确分类 率接近90%左右,说明了提出方法的有效性。     

A new pyramidal opponent color-shape model based video shot boundary detection

Video shot boundary detection (VSBD) is one of the most essential criteria for many intelligent video analysis-related applications, such as video retrieval, indexing, browsing, categorization and summarization. VSBD aims to segment big video data into meaningful fragments known as shots. This paper put forwards a new pyramidal opponent colour-shape (POCS) model which can detect abrupt transition (AT) and gradual transition (GT) simultaneously, even in the presence of illumination changes, huge object movement between frames, and fast camera motion. First, the content of frames in the video subjected to VSBD is represented by the proposed POCS model. Consequently, the temporal nature of the POCS model is subjected to a suitable segment (SS) selection procedure in order to minimize the complexity of VSBD method. The SS from the video frames is examined for transitions within it using a bagged-trees classifier (BTC) learned on a balanced training set via parallel processing. To prove the superiority of the proposed VSBD algorithm, it is evaluated on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of video according to no transition (NT), AT and GT. The experimental evaluation results in an F₁-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets, respectively.     

基于压缩传感和加权主色的镜头边界检测算法

针对现有算法存在计算量大、对摄像机和物体运动 敏感等缺点,提出一种基于压缩传感(CS)和加权主色(WMC)的镜头边界检测(SBD)算法(CSWMC )。首先通过直方图特征得到粗略的镜头 边界集合;然后利用CS将该集合中的帧及其前后帧的高维特征投影到低维空间,采用调节余 弦相似度得到基 于夹角的第一判定指标;继而定义一种新的图像主色权值和基于该权值的类Bhattacharyya 相似度,得到基于颜色 相似度的第二判定指标;以两种判定指标的乘积作为最终判定指标,并设计一种朴素但有效 的策略进行SBD。 实验结果表明,与常用方法相比,所提算法具有更高的查全率和精确率,能够更加有效进行 SBD。     

用Hausdorff距离进行视频镜头边界检测

提出了一种新的利用Ｈａｕｓｄｏｒｆｆ距离进行视频镜头边界检测的方法。首先使用Ｃａｎｎｙ乍子提取视频序列的边缘,把当前帧的边缘图分成Ｎ个小区域,分别进行运动补偿,然后利用Ｈａｕｓｄｏｒｆｆ距离比较视频帧之间边缘特性的差异,这种差异体现在各个小区域新出现和刚消失边缘点的数量之和。这种方法不但受光照条件变化影响不大,而且也能在某种程度中抵消物体和镜头的运动,最后讨论了该算法的局限性和今后的改进。     

镜头变换边界复合检测算法研究

将视频图像进行有效时间分段及标定是视频图像内容分析、随机接入、视频数据库浏览与检索的重要研究内容。在对视频图像进行时间分段和标定过程中,镜头变换边界的自动检测十分重要。镜头变换边界的自动检测算法在国际上得到了广泛的研究,在分析了当前国际上的镜头变换边界检测算法的基础上,提出了一种镜头变换边界复合检测算法。理论分析及实验结果表明该复合检测算法不仅具有实现复杂度较低,而且对各类型图像序列检测性能好的特点。     

两种镜头检测算法的对比

对于基于内容的视频检索(CBVR)、索引、浏览等系统来说,镜头检测是第一步也是关键的一步.本文首先介绍了基于内容的视频检索系统的研究价值、镜头检测的分类,以及镜头检测算法的一般步骤.然后对两种镜头检测算法进行了详细介绍,一种是比较复杂的基于小波变换的算法,另一种是较为简单的基于颜色直方图的算法,按照这两种算法在MATL...     

Robust detection with the gap metric

In a multipath communication channel, the optimal receiver is matched to the maximum likelihood (ML) estimate of the multipath signal. In general, this leads to a computationally intensive multidimensional nonlinear optimization problem that is not feasible in most applications. We develop a detection algorithm that avoids finding the ML estimates of the channel parameters while still achieving good performance. Our approach is based on a geometric interpretation of the multipath detection problem. The ML estimate of the multipath signal is the orthogonal projection of the received signal on a suitable signal subspace S. We design a second subspace G, which is the representation subspace, that is close to S but whose orthogonal projection is easily computed. The closeness is measured by the gap metric. The subspace G is designed by using wavelet analysis tools coupled with a reshaping algorithm in the Zak transform domain. We show examples where our approach significantly outperforms the conventional correlator receiver (CR) and other alternative suboptimal detectors     

Orientation contrast model for boundary detection

The boundary detection task has been extensively studied in the field of computer vision and pattern recognition. Recently, researchers have formulated this task as supervised or unsupervised learning problems to leverage machine learning methods to improve detection accuracy. However, texture suppression, which is important for boundary detection, is not incorporated in this framework. To address this limitation, and also motivated by psychophysical and neurophysiological findings, we propose an orientation contrast model for boundary detection, which combines machine learning technique and texture suppression in a unified framework. Thus, the model is especially suited for detecting object boundaries surrounded by natural textures. Extensive experiments on several benchmarks demonstrate the improved boundary detection performance of the model. Specifically, its detection accuracy was improved by 10% on the Rug dataset compared with state-of-the-art unsupervised boundary detection algorithm, and its performance is also better or at least comparable with previous supervised boundary detection algorithms.     

MAP metric for blind phase sequence detection in selected mapping

Selected mapping (SLM) is a promising technique to reduce the high peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. In this paper, we derive the optimal phase sequence detection metric for blind selected mapping (BSLM) and show that the minimum distance metric is suboptimal. We then define a certain selection criterion in the transmitter which assigns different probabilities to different mapping sequences. These probabilities are integrated into the optimal metric to achieve several dBs of SNR improvement in blind phase sequence detection.     

基于改进BEMD的视频镜头转场检测算法研究

对二维经验模式分解(BEMD)算法进行了改进,采用限定域的Delaunay三角剖分和三次插值得到极大值和极小值包络面,用基于限邻域经验模式分解(NLEMD),即通过设定最大邻域(时宽)和采用邻域内局部自适应均值算法代替包络均值算法进行分解,给出了图像BEMD分解后内蕴模函数(I MF)1和2的Hilbert谱,以I MF2的瞬时振幅作为图像的特征向量,计算镜头转场中图像序列帧特征向量间的欧式距离。采用大量的视频镜头转场的样本进行实验,结果表明,剪切镜头查准率和查全率皆为98%;渐变镜头查准率86.4%,查全率87.6%。     

Video shot detection and condensed representation. a review

There is an urgent need to develop techniques that organize video data into more compact forms or extract semantically meaningful information. Such operations can serve as a first step for a number of different data access tasks such as browsing, retrieval, genre classification, and event detection. In this paper, we focus not on the high-level video analysis task themselves but on the common basic techniques that have been developed to facilitate them. These basic tasks are shot boundary detection and condensed video representation     

改进的近地谐波探测传播模型

对工作于近地条件的谐波探测系统,自由空间假设下导出的谐波雷达方程已经不足以预测和解释观测过程中的诸多现象,故本文基于目标特性和环境因素研究了这类系统的修正传播模型。一是引入目标谐波特性修正,使得接收谐波随距离的衰减率更加符合实际测量;二是引入基波和谐波的多径效应分析,使得模型具备预测和解释近地观测的接收功率振荡现象。此外进行仿真分析并搭设了测量系统,设计了室内近程和室外长程的实测实验,验证了传播模型所预测的探测盲区现象,并提出了对其加以缓解和利用的思路构想,对近地谐波探测系统设计和性能评估具有积极意义.     

基于Hough变换改进的直线检测与定位

利用标准的Hough变换检测空间的直线,研究各种改进的Hough变换算法,设计一种新的Hough变换检测算法,通过实验对比,从而获得更高的精度。     

基于度量学习的意图识别和槽填充方法

人机对话中小样本学习场景下的意图识别和槽填充,是自然语言处理的一个重要课题.本文采用基于度量学习的方法,通过计算query set中的样本与support set中样本的距离,寻找距离最近的类别样本作为分类标签,同时将两个任务联合进行训练,用以提升模型的效果.从实验结果中可以得出,本文提出的Fine-tune方法,对意图识别和槽填充任务都有一定的帮助和提升.胶囊网络在意图识别中也起到了一定的效果,可以帮助去除一部分无关信息,但对槽填充任务的帮助不明显;而任务自适应的投影网络,可以更好地将不同类的向量分开,提升了两个任务的性能.