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。     

A machine learning based intelligent vision system for autonomous object detection and recognition

Existing object recognition techniques often rely on human labeled data conducting to severe limitations to design a fully autonomous machine vision system. In this work, we present an intelligent machine vision system able to learn autonomously individual objects present in real environment. This system relies on salient object detection. In its design, we were inspired by early processing stages of human visual system. In this context we suggest a novel fast algorithm for visually salient object detection, robust to real-world illumination conditions. Then we use it to extract salient objects which can be efficiently used for training the machine learning-based object detection and recognition unit of the proposed system. We provide results of our salient object detection algorithm on MSRA Salient Object Database benchmark comparing its quality with other state-of-the-art approaches. The proposed system has been implemented on a humanoid robot, increasing its autonomy in learning and interaction with humans. We report and discuss the obtained results, validating the proposed concepts.     

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.     

Relative Flow Estimates for Shot Boundary Detection

This paper proposes a simple approach based on Relative Flow Estimates (RFE) for shot cut detection. The property of Relative flow estimates can be used for abrupt cut detection and a correction mechanism for gradual camera-shot transition detection (e.g., fade-in and fade-out, dissolves, wipes). The exacted feature vector in each frame can be mapped into a 3-D space along the continuous time axis, and these feature data can be treated as a virtually constructed pipe with fluid flowing in the 3-D axis. Compared with existing approaches, the new RFE-based algorithm can directly detect shot cut. A wide range of test videos are used to evaluate the performance of the proposed method. The experimental results show that the new scheme can produce promising results.     

基于运动信息的镜头切变检测算法

镜头检测算法的研究是视频分析与检索中较为活跃的研究领域之一。该文提出了一种基于运动信息的镜头切变检测算法，该算法采用四叉树算法分析视频序列的运动信息，进而提取基于运动信息的特征矢量，由提取的特征矢量可以检测出视频序列中的镜头切变点。实验表明该算法是有效的，可行的，并且具有非常强的抗干扰能力。     

视频镜头时域分割方法的研究

视频时域分割指将视频序列分成若干镜头，是视频内容分析以及基于内容的视频浏览和检索的第一步．该文首先对视频结构以及视频镜头种类进行了简要的描述，然后对为计算不连续性而采用的提取特征和建立测量准则的常用方法进行概述．其后，文章介绍了检测镜头切变和渐变的算法及其优缺点．在压缩域上检测镜头变换边界的问题也在文中予以分析．在结论与展望中，提出了一些这一领域的难点和对今后工作的展望．     

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。     

基于互信息量的镜头边界检测算法

为快速稳健地检测镜头边界,提出基于互信息量的镜头边界检测算法。采用基于HSV空间不均匀分块直方图求取的帧间互信息量作为视频帧间差异度量,结合相应的阈值策略与时域窗策略,实现对镜头的切变,以及常见渐变与计算机特效技术制作的镜头变换检测。对包括广告、综艺节目、新闻等多类视频数据进行实验,结果证明,该算法对切变检测的综合效果比经典基于直方图的双阈值法平均提高11.9%,渐变检测的综合效果平均提高7.6%,能有效减弱摄像机运动与光照变化对镜头检测的影响,对不同类型视频镜头检测效果稳定,具有较高的查全率与查准率。     

图像内容感知缩放的检测方法研究

针对可用于图像篡改的内容感知缩放技术,本文提出了一种基于概率Map图统计特征的内容感知缩放检测算法.该算法利用概率Map图来反映图像是否经过内容感知缩放操作,并利用新提出的积分投影与局部统计特征来检测篡改图像.而后利用分类器进行分类训练,从而有效识别基于内容感知缩放操作的图像篡改.实验结果显示,所提算法能够区分出原始图像与篡改图像,并具有较高的正确检测率.     

一种基于宏块类型匹配的镜头切换检测算法

提出了一种新的MPEG压缩视频镜头切换检测算法。该方法通过分析切换点附近各帧的宏块类型特征，引入了一种新的“宏块类型匹配符”（MTMO），使用其在宏块级别上度量子图片组（SGOP）内部各帧之间的差异性，并结合一种自适应阈值选取技术，完成镜头切换检测。实验结果表明，所提算法适用于不同SGOP结构的MPEG视频序列，查全率达99％，准确率达95％。     

A Supervised Learning Approach to Flashlight Detection

Shot boundary detection is a fundamental step of video indexing. One crucial issue of this step is the discrimination of abrupt shot change from flashlight, because flashlight often induces a false shot boundary. Support vector machine (SVM) is a supervised learning technique for data classification. In this paper, we propose a SVM-based technique to detect flashlights in video. Our approach to flashlight detection is based on the facts that the duration of flashlight is short and the video contents before and after a flashlight should be similar. Therefore, we design a sliding window in temporal domain to monitor the instantaneous video variation and extract color and edge features to compare the visual contents between two video segments. Then, a SVM is employed to classify the luminance variation into flashlight or shot cut. Experimental results indicate that the proposed approach is effective and outperforms some existing techniques.     

基于代价敏感集成分类器的长方法检测

长方法(Long Method)是由于一个方法太长而需要重构的软件设计的问题。为了提高传统机器学习方法对长方法的识别率,针对代码坏味数据不平衡的特性,提出代价敏感集成分类器算法。以传统决策树算法为基础,利用欠采样策略对样本进行重采样,进而生成多个平衡的子集,并将这些子集训练生成多个相同的基分类器,然后将这些基分类器组合形成一个集成分类器。最后在集成分类器中引入由认知复杂度决定的误分类代价,使得分类器向准确分类少数类倾斜。与传统机器学习算法相比,此方法对长方法检测结果的查准率和查全率均有一定提升。     

A fuzzy logic approach for detection of video shot boundaries

Video temporal segmentation is normally the first and important step for content-based video applications. Many features including the pixel difference, colour histogram, motion, and edge information etc. have been widely used and reported in the literature to detect shot cuts inside videos. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries with one single algorithm. In this paper, we propose a fuzzy logic approach to integrate hybrid features for detecting shot boundaries inside general videos. The fuzzy logic approach contains two processing modes, where one is dedicated to detection of abrupt shot cuts including those short dissolved shots, and the other for detection of gradual shot cuts. These two modes are unified by a mode-selector to decide which mode the scheme should work on in order to achieve the best possible detection performances. By using the publicly available test data set from Carleton University, extensive experiments were carried out and the test results illustrate that the proposed algorithm outperforms the representative existing algorithms in terms of the precision and recall rates.     

一种因果的突变镜头检测方法

通常镜头检测算法在进行镜头检测时都要利用前后若干帧的信息,该文提出了一种因果的突变镜头检测方法,只利用当前帧之前有限长度内帧的信息。利用两帧之间3×3分块后对应区间HSV直方图间的距离,应用加权系数来计算帧间差值。运用位于当前帧前的一个可变长的滑动窗口来进行突变镜头检测,并对静止镜头进行了处理。该文最后给出了算法的完整流程并进行了试验,试验结果表明该算法的检测准确率达到了95%,并对推拉镜头具有一定的鲁棒性。     

基于流形特征的镜头边界检测后处理算法

镜头是视频分析和检索的基础,为了有效检测镜头边界,并对镜头边界进行分类,提出一种新的镜头边界描述子：镜头边界流形．视频流可以看做一个高雏空间,镜头边界流形是隐含在该空间中的局部结构,它能够有效区分镜头边界和非镜头边界,以及区分不同类型的镜头边界．采用这种特征,提出一种后处理算法,一方面去除预检测镜头边界的虚警,另一方面将真正的镜头边界分类．实验结果证实了镜头边界流行的有效性以及后处理算法的优异性能．     

Content-aware rate allocation for efficient video streaming via dynamic network utility maximization

Nowadays it is vital to design robust mechanisms to provide QoS for multimedia applications as an integral part of the network traffic. The main goal of this paper is to provide an efficient rate control scheme to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in congested networks. Towards this, we introduce a content-aware time-varying utility function, in which the quality impact of video content is incorporated into its mathematical expression. Moreover, we analytically model the buffer requirements of video sources in two ways: first as constraints of the optimization problem to guarantee a minimum rate demand for each source, and second as a penalty function embedded as part of the objective function attempting to achieve the highest possible rate for each source. Then, using the proposed analytical model, we formulate a dynamic network utility maximization problem, which aims to maximize the aggregate hybrid objective function of sources subject to capacity and buffer constraints. Finally, using primal–dual method, we solve DNUM problem and propose a distributed algorithm called CA-DNUM that optimally allocates the shared bandwidth to video streams. The experimental results demonstrate the efficacy and performance improvement of the proposed content-aware rate allocation algorithm for video sources in different scenarios.     

An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the advantage of support vector machine (SVM), decision tree (DT), and simulated annealing (SA). In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection. By analyzing the information from using KDD’99 dataset, DT and SA can obtain decision rules for new attacks and can improve accuracy of classification. In addition, the best parameter settings for the DT and SVM are automatically adjusted by SA. The proposed algorithm outperforms other existing approaches. Simulation results demonstrate that the proposed algorithm is successful in detecting anomaly intrusion detection.     

一种基于有限自动机的渐变镜头检测算法

渐变镜头检测算法分为两个方面：渐变边界帧的判定和边界帧的组合。前者判断某一帧是否符合渐变边界帧的每件，后者判断一段包含边界帧的视像是否是渐变。以往的算法侧重解决边界帧的判定，忽视了边界帧的组合。本文定义了渐变检测容忍度的概念，并提出了一种基于有限自动机的渐变镜头检测方法，利用了自动机多状态的记忆性，提高了算法的适应性和鲁棒性。在TRECVID2004的SBD项目中，本渐变镜头检测系统取得了渐变检测性能第一的好成绩。     

基于粗糙集和模糊聚类的新闻视频镜头边界检测方法

为了将视频分割成镜头,目前的方法都是提取某些特征然后构造不同的相异性函数。然而,太多的特征就会降低镜头分割算法的效率。因此,有必要对每一个镜头检测决策进行特征约简。基于此,提出了基于粗糙集和模糊聚类的分类方法并得到了相应的决策规则。针对新闻场景的特殊性,将镜头分割成突变过渡、渐变过渡以及无场景变化3类。用超过2个小时的新闻视频所做的实验获得了96.5%的查全率和97.9%的准确率。