视频概要的分类与综合评价方法

针对视频摘要产生过程,从结果、信息源、特征化级别和粒度四个方面,提出了一种分类方法,给出了若干实例进行综合评价,侧重反映不同视频概要所处的技术层次与水平。在此基础上,分析了研究现状,给出了进一步研究的方向。     

视频结构挖掘的概念及应用

提出了一种视频结构挖掘的概念框架和视频结构挖掘系统框架,在概念框架中对视频结构挖掘相关概念给出了规范化的定义,视频结构挖掘框架包括的主要内容有视频基本结构挖掘、视频语法结构挖掘和视频语义结构挖掘。最后讨论了视频结构挖掘中发现的结构模式和知识的具体应用,包括指导视频的组织与管理、实现基于内容的个性视频推荐和改善视频摘要系统。     

视频认证技术的研究

该文对数字图像认证技术在视频中的扩展应用——视频认证技术的背景、特点、分类和现有算法做了一个综—述,对现有的两类视频认证方法:数字签名和数字水印进行了分析和比较,给出了运用这两种技术进行视频认证时的基本方法、已有的典型算法及需要解决的关键问题,并对视频认证技术未来研究方向进行了展望。     

基于DM642的视频处理系统设计

本文介绍了一种基于TMS320DM642和Philips视频编解码芯片的视频处理系统,给出了系统的硬件设计、视频接口连接图、以及软件配置。通过实际应用证明该方案具有高速视频输入输出能力,为视频编解码算法开发、视频处理产品设计搭建了高性能的平台。     

视频存储技术发展综述

网络技术的不断发展,远程视频的应用研究成为计算机领域的一个研究热点,而视频存储技术的研究是这一领域内研究最早、发展较为成熟的一部分。文章主要综述和比较了20世纪90年代中期以来视频存储方面的关键技术,并对这些技术的优缺点及使用的局限性给出了评说。     

基于纹理合成的视频去抖动

视频去抖动是视频增强技术的一个重要应用,通过纠正视频帧的位置使视频运动变得平稳。随之而来的问题是如何修复视频帧留下的空缺以保持视频的连续性。在对图像修复技术进行研究的基础上,提出了利用改进的纹理合成技术进行去抖动视频修复的方法。实验给出的视频去抖动效果证明了该方法的有效性。     

基于内容的监视视频摘要技术研究

介绍了视频摘要的相关概念、作用以及视频摘要所涉及到的一些关键技术,探讨了视频结构化的主要步骤,在分析监视视频特征的基础上,给出了一个基于内容的监视视频摘要系统模型。     

基于内容的监视视频摘要技术研究

介绍了视频摘要的相关概念、作用以及视频摘要所涉及到的一些关键技术,探讨了视频结构化的主要步骤,在分析监视视频特征的基础上,给出了一个基于内容的监视视频摘要系统模型。     

一种家庭视频摘要生成的新方法

计算机硬件的发展使家用计算机具有处理和存储视频资料的能力，而家用数字摄像设备的普及使家庭视频的数量越来越多，家庭用户对视频摘要技术的需求也越来越强烈。在回顾现有视频摘要相关的概念、分类和技术，以及分析家庭视频的特征基础上，给出了家庭视频摘要的特点，并提出了一个面向家庭视频的视频摘要算法。     

视频结构挖掘的概念和框架

视频信息处理的关键是视频信息的结构化，视频除了有基本层次结构之外，还有隐藏其中的视频结构语法和结构语义。该文提出了一种视频结构挖掘的概念框架和视频结构挖掘的系统框架，在概念框架中对视频结构挖掘相关概念给出了明确定义和界定；视频结构挖掘框架主要包括：视频基本层次结构挖掘，视频结构语法挖掘和视频结构语义挖掘。讨论了视频结构模式和知识的具体应用，包括指导视频的组织与管理、实现基于内容的个性视频推荐和改善视频摘要系统。     

VISON: VIdeo Summarization for ONline applications

Recent advances in technology have increased the availability of video data, creating a strong requirement for efficient systems to manage those materials. Making efficient use of video information requires that data to be accessed in a user-friendly way. This has been the goal of a quickly evolving research area known as video summarization. Most of existing techniques to address the problem of summarizing a video sequence have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are two extremely time-consuming tasks. Thus, video summaries are usually produced off-line, penalizing any user interaction. The lack of customization is very critical, as users often have different demands and resources. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present VISON, a novel approach for video summarization that works in the compressed domain and allows user interaction. The proposed method is based on both exploiting visual features extracted from the video stream and on using a simple and fast algorithm to summarize the video content. Results from a rigorous empirical comparison with a subjective evaluation show that our technique produces video summaries with high quality relative to the state-of-the-art solutions and in a computational time that makes it suitable for online usage.     

Exploring video content structure for hierarchical summarization

In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot- segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units. A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.Published online: 15 September 2004 Corespondence to: Xingquan ZhuThis research has been supported by the NSF under grants 9972883-EIA, 9974255-IIS, 9983248-EIA, and 0209120-IIS, a grant from the state of Indiana 21^th Century Fund, and by the U.S. Army Research Laboratory and the U.S. Army Research Office under grant DAAD19-02-1-0178.     

The priority curve algorithm for video summarization

In this paper, we introduce the concept of a priority curve associated with a video. We then provide an algorithm that can use the priority curve to create a summary (of a desired length) of any video. The summary thus created exhibits nice continuity properties and also avoids repetition. We have implemented the priority curve algorithm (PriCA) and compared it with other summarization algorithms in the literature with respect to both performance and the output quality. The quality of summaries was evaluated by a group of 200 students in Naples, Italy, who watched soccer videos. We show that PriCA is faster than existing algorithms and also produces better quality summaries. We also briefly describe a soccer video summarization system we have built on using the PriCA architecture and various (classical) image processing algorithms.     

Video summarization and retrieval using singular value decomposition

In this paper, we propose novel video summarization and retrieval systems based on unique properties from singular value decomposition (SVD). Through mathematical analysis, we derive the SVD properties that capture both the temporal and spatial characteristics of the input video in the singular vector space. Using these SVD properties, we are able to summarize a video by outputting a motion video summary with the user-specified length. The motion video summary aims to eliminate visual redundancies while assigning equal show time to equal amounts of visual content for the original video program. On the other hand, the same SVD properties can also be used to categorize and retrieve video shots based on their temporal and spatial characteristics. As an extended application of the derived SVD properties, we propose a system that is able to retrieve video shots according to their degrees of visual changes, color distribution uniformities, and visual similarities.     

Keyframe-based video summarization using Delaunay clustering

Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.     

基于对象的视频摘要技术

视频摘要技术是当前多媒体领域研究的热点之一。视频摘要生成方法归结为两类:基于关键帧的视频摘要和基于对象的视频摘要;对基于关键帧的视频摘要方法做了简要的介绍,并重点总结了历年来出现的基于对象的视频摘要的生成方法。最后对视频摘要技术的发展做出了总结和展望。