基于语义的视频检索技术综述

本文综述了基于语义的视频检索的研究现状,以帮助未来的研究人员了解基于语义的视频检索领域中可用的技术,视频检索系统的产生是为了在互联网或数据库中的大量视频数据集中找到用户想要查询的视频.本文对基于语义的视频检索过程进行了说明与讨论,本文还对基于语义的视频检索中,解决语义鸿沟这一主要问题的相关技术进行了综述.语义鸿沟的形成是因为从视频内容中提取的低层特征与现实世界中用户对这些特征的认知存在差异,将视频内容的低层特征转化为高层的语义概念是一个备受关注的研究课题.     

语义视频检索综述

视频内容检索是多媒体应用的一个活跃研究方向，现有的内容检索技术大多是基于低层次特征的。这些非语义的低层特征难以理解，与人思维中的高层语义概念相差甚远，严重影响视频内容检索系统的易用性。低层特征和高层语义概念间的语义鸿沟很难逾越。如何跨越语义鸿沟，用语义概念检索视频内容是目前基于内容视频检索最具挑战性的研究方向。本文介绍语义视频检索出现的背景，分析语义鸿沟出现的原因，对现有尝试跨越语义鸿沟的主要方法进行综述；评述了相关技术的优缺点，探讨了各方法将来可能的研究发展方向以及视频语义检索近期、长期可能的技术突破点。     

利用本体的视频语义概念检测

视频数据的不断丰富以及人们对视频检索的要求越来越复杂,使得视频语义信息建模和高层语义概念提取逐渐成为视频检索中的重要组成部分.本文提出一种基于本体的视频语义概念检测方法,利用贝叶斯网络构造视频中概念语义关系的检测本体,构建了视频中概念之间的层次关系,并能够通过推理完成复合语义概念的检测.该方法从语义信息学的角度对视频内容进行分析,在一定程度上削弱了语义鸿沟的影响,并且取得了较好的查询结果.     

基于高层语义的视频检索研究

视频语义检索的研究是目前研究的热点之一。现有的视频检索系统技术多是基于底层特征的、非语义层次的检索。与人类思维中所能理解的高层语义概念相去甚远,这严重影响视频检索的实际效果。如何跨越底层特征和高层语义的鸿沟,用高层语义概念进行视频检索是当前研究的重点。通过对视频内容的语义理解、语义分析、语义提取的简要概述,试图构造一种视频语义检索模型。     

基于领域本体的新闻视频检索

新闻视频作为视频数据中有代表性的一种媒体,受到人们的广泛关注,对新闻视频的检索要求也越来越高.传统的新闻视频检索大多是非语义层面的,采用的是基于关键词的检索方法，难于获得令人满意的查准率和查全率．本文提出一种基于领域本体的新闻视频检索框架，定义了新闻视频检索中的新闻视频对象，使用语义表达能力强的领域本体来指导视频语义对象的标注，并针对“一词多义”问题提出了“概念域-概念”两阶段概念消歧算法；针对自然语言检索问题，使用领域本体进行查询优化和查询扩展，并提出了查询语句自动生成方法．实验表明，基于领域本体的新闻视频检索方法可以有效的提高检索性能．     

基于视觉词袋的视频检索校准方法

随着互联网技术的飞速发展,视频数据呈现海量爆炸式增长,传统的视频搜索引擎 多数采用单一的基于文本的检索方法,该检索方法对于视频这类非结构化数据,存在着内容缺失、 语义隔阂等问题,导致检索结果相关度较低。提出一种基于视觉词袋的视频检索校准方法,该方 法结合了视频数据的可视化特征提取技术、TF-IDF 技术、开放数据技术,为用户提供优化后的 视频检索校准结果。首先,基于HSV 模型的聚类算法提取视频的关键帧集合及关键帧权值向量; 接着用关键帧图像的加速稳健特征等表示视频的内容特征,解决视频检索的内容缺失问题;然后 利用TF-IDF 技术衡量查询语句关键字的权值,并开放数据获得查询语句关键字的可视化特征和 语义信息,解决视频检索的语义隔阂问题;最后,将提出的基于视觉词袋的视频检索校准算法应 用于Internet Archive 数据集。实验结果表明,与传统的基于文本的视频检索方法相比,该方法的 平均检索结果相关度提高了15%。     

一种基于本体的视频检索技术研究

传统的视频检索大多采用基于关键词的方法,难以获得让用户满意的查准率和查全率。为此提出一种基于本体的视频检索技术,该技术借助于领域本体,以其基本概念为关键词通过互联网图像搜索引擎在线获取样本图像组,提取SIFT特征建立图像特征词典,抽取图像特征直方图并计算相似度,辅助完成视频的自动标注,初始化视频检索库;同时,借助于领域本体,对从用户的查询输入中抽取的关键词进行语义扩展,将以扩展概念集进行检索的结果返回给用户,以此实现基于本体的视频检索。最后,结合实例对该算法进行实现和分析,表明了该方法的可行性和有效性。     

基于概念对关系的视频多语义概念学习方法研究

多语义概念学习是视频检索的重要支持技术。针对此问题提出了基于概念对关系的视频多语义概念学习方法。首先分析大规模语义概念之间存在的概念对关系类型,并根据TRECVID2005标注数据以及Columbia374数据对其进行定量分析;然后利用概念对关系进行基于上下文关系语义概念探测器的相关概念选择,并根据探测器可靠性对相关概念赋予权重,最后根据视觉相似性和单语义概念探测器进行融合。实验证明,该方法能够取得较好的语义概念探测性能。     

一种基于贝叶斯网络的视频静态语义探测新方法

传统的基于内容的视频检索是利用图像的颜色、纹理以及形状等底层特征来对视频进行检索,然而这些底层特征并没有深层次地挖掘出视频的语义内容。在用支持矢量机对图像进行分类的基础上,提出了一种基于贝叶斯网络的对视频静态语义如室内/室外进行探测的新方法,实验结果验证该方法的有效性。     

一种基于贝叶斯网络的视频静态语义探测新方法

传统的基于内容的视频检索是利用图像的颜色、纹理以及形状等底层特征来对视频进行检索,然而这些底层特征并没有深层次地挖掘出视频的语义内容。在用支持矢量机对图像进行分类的基础上,提出了一种基于贝叶斯网络的对视频静态语义如室内/室外进行探测的新方法,实验结果验证该方法的有效性。     

The semantic pathfinder: using an authoring metaphor for generic multimedia indexing

This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives.     

Semantic Extraction and Semantics-Based Annotation and Retrieval for Video Databases

Digital video databases have become more pervasive and finding video clips quickly in large databases becomes a major challenge. Due to the nature of video, accessing contents of video is difficult and time-consuming. With content-based video systems today, there exists a significant gap between the user's information and what the system can deliver. Therefore, enabling intelligent means of interpretation on visual content, semantics annotation and retrieval are important topics of research. In this paper, we consider semantic interpretation of the contents as annotation tags for video clips, giving a retrieval-driven and application-oriented semantics extraction, annotation and retrieval model for video database management system. This system design employs an algorithm on objects' relation and it can reveal the semantics defined with fast real-time computation.     

An integrated semantic-based approach in concept based video retrieval

Multimedia content has been growing quickly and video retrieval is regarded as one of the most famous issues in multimedia research. In order to retrieve a desirable video, users express their needs in terms of queries. Queries can be on object, motion, texture, color, audio, etc. Low-level representations of video are different from the higher level concepts which a user associates with video. Therefore, query based on semantics is more realistic and tangible for end user. Comprehending the semantics of query has opened a new insight in video retrieval and bridging the semantic gap. However, the problem is that the video needs to be manually annotated in order to support queries expressed in terms of semantic concepts. Annotating semantic concepts which appear in video shots is a challenging and time-consuming task. Moreover, it is not possible to provide annotation for every concept in the real world. In this study, an integrated semantic-based approach for similarity computation is proposed with respect to enhance the retrieval effectiveness in concept-based video retrieval. The proposed method is based on the integration of knowledge-based and corpus-based semantic word similarity measures in order to retrieve video shots for concepts whose annotations are not available for the system. The TRECVID 2005 dataset is used for evaluation purpose, and the results of applying proposed method are then compared against the individual knowledge-based and corpus-based semantic word similarity measures which were utilized in previous studies in the same domain. The superiority of integrated similarity method is shown and evaluated in terms of Mean Average Precision (MAP).     

Video model for dynamic objects

Content based retrieval from a video database depends greatly on how the semantics of video is described. In this paper we propose a video model that tries to capture the semantics of video segments based on the dynamic characteristics of the objects present in the segments. State-chart based formalism has been adopted to describe the objects of a video and their dynamic behavior. Several operators have been defined to operate upon the video segments. The video calculus introduced here helps in representing the semantics of the segments using first-order predicates. Formal semantics of this language has been analyzed and a simple implementation scheme for video databases has been proposed.     

基于多模态信息挖掘融合的视频检索技术

基于内容的多媒体检索特别是视频检索,由于多媒体数据本身具有复杂的语义,所以极大地提高了检索的难度.算法着眼于视频本身挖掘出充分的资源信息并且将这些信息加以融合来提高视频检索的性能.基于这种思想,提出一种多模态视频检索模型以及相应的手动式搜索和交互式搜索的算法方案.搜索策略在TRECVID视频检索比赛中取得了不错的成绩.     

一种基于语义的视频场景分割算法

针对如何在镜头基础上进行聚类，以得到更高层次的场景问题，提出了一个基于语义的场景分割算法。该算法首先将视频分割为镜头，并提取镜头的关键帧。然后计算关键帧的颜色直方图和MPEG-7边缘直方图，以形成关键帧的特征；接着利用镜头关键帧的颜色和纹理特征对支持向量机（SVM）进行训练来构造7个基于SVM对应不同语义概念的分类器，并利用它们对要进行场景分割的视频镜头关键帧进行分类，以得到关键帧的语义。并根据关键帧包含的语义概念形成了其语义概念矢量，最后根据语义概念矢量通过对镜头关键帧进行聚类来得到场景。另外．为提取场景关键帧，还构建了镜头选择函数，并根据该函数值的大小来选择场景的关键帧。实验结果表明，该场景分割算法与Hanjalic的方法相比，查准率和查全率分别提高了34．7％和9．1％。