【视频相似性检测算法探究文献综述5200字】

视频相似性检测算法研究国内外文献综述目录TOC\o"1-2"\h\u2946视频相似性检测算法研究国内外文献综述 167051视频分类算法研究现状 1218372视频关键帧选取研究现状 2278203视频相似性判定及检测算法研究现状 34584(1)视频相似性判定 320444(2)视频指纹提取 332250(3)视频相似性检测 410466（4）现存问题及解决 61视频分类算法研究现状视频分类将待检测视频分入与其类别相对应的小类目中，加快视频相似性检测的速度，是计算机视觉领域和数字媒体应用中的一个热点研究课题。短视频是一类内容庞杂的视频数据，如何在海量短视频数据中寻找到有效信息一直是用户关注的问题，由此产生了视频索引、视频检索、视频相似性检测等相关应用。视频分类是指给定一个视频片段，对其中所展示的内容进行分类，通过视频分类进行视频的预处理是快速获取有效信息的一种常用手段。视频分类技术将同类型视频分为小类目，对于创作者而言快速检索到相同短视频有助于进行版权保护，对于视频平台而言较为精确的分类有助于进行优化推荐算法。目前的视频分类的主要分类研究方向是以视频中人物做出的动作为研究切入点进行分类ADDINEN.CITE<EndNote><Cite><Author>Soomro</Author><Year>2012</Year><RecNum>129</RecNum><DisplayText><styleface="superscript">[3-5]</style></DisplayText><record><rec-number>129</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618404154">129</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Soomro,K.</author><author>Zamir,A.R.</author><author>Shah,M.%JComputerScience</author></authors></contributors><titles><title>UCF101:ADatasetof101HumanActionsClassesFromVideosinTheWild</title></titles><dates><year>2012</year></dates><urls></urls></record></Cite><Cite><Author>Kuehne</Author><Year>2011</Year><RecNum>130</RecNum><record><rec-number>130</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618404299">130</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Kuehne,H.</author><author>Jhuang,H.</author><author>Garrote,E.</author><author>Poggio,T.</author><author>Serre,T.</author></authors></contributors><titles><title>HMDB:ALargeVideoDatabaseforHumanMotionRecognition</title><secondary-title>IEEEInternationalConferenceonComputerVision</secondary-title></titles><dates><year>2011</year></dates><urls></urls></record></Cite><Cite><Author>Heilbron</Author><RecNum>131</RecNum><record><rec-number>131</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618404466">131</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Heilbron,F.C.</author><author>Escorcia,V.</author><author>Ghanem,B.</author><author>Niebles,J.C.%JIEEE</author></authors></contributors><titles><title>ActivityNet:Alarge-scalevideobenchmarkforhumanactivityunderstanding</title></titles><dates></dates><urls></urls></record></Cite></EndNote>[\o"Soomro,2012#129"3-5]。早期针对视频分类主要采用的方法是将视频以帧的形式进行存储，通过对单帧的图像场景或人物动作进行分类，从而达到对视频进行分类的目的。即识别动作靠场景，识别场景靠动作，在UCF101数据集的准确率最高只有68.7%。近年来，随着深度学习的发展，2014年AndrejKarpathy等人ADDINEN.CITE<EndNote><Cite><Author>Karpathy</Author><Year>2014</Year><RecNum>122</RecNum><DisplayText><styleface="superscript">[6]</style></DisplayText><record><rec-number>122</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618402196">122</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Karpathy,A.</author><author>Toderici,G.</author><author>Shetty,S.</author><author>Leung,T.</author><author>Li,F.F.</author></authors></contributors><titles><title>Large-ScaleVideoClassificationwithConvolutionalNeuralNetworks</title><secondary-title>ComputerVision&PatternRecognition</secondary-title></titles><dates><year>2014</year></dates><urls></urls></record></Cite></EndNote>[\o"Karpathy,2014#122"6]将时序语义融合的方法分为四种。2015年TRAN等人ADDINEN.CITE<EndNote><Cite><Author>Tran</Author><Year>2014</Year><RecNum>121</RecNum><DisplayText><styleface="superscript">[7]</style></DisplayText><record><rec-number>121</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618400459">121</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Tran,D.</author><author>Bourdev,L.</author><author>Fergus,R.</author><author>Torresani,L.</author><author>Paluri,M.</author></authors></contributors><titles><title>LearningSpatiotemporalFeatureswith3DConvolutionalNetworks</title></titles><dates><year>2014</year></dates><urls></urls></record></Cite></EndNote>[\o"Tran,2014#121"7]提出C3D网络，利用三维卷积提取连续帧序列的时空域特征进行视频分类，在UCF101数据集上准确率最高为85.2%，FPS为313.9。在优化过程中，2017年CARREIRA等人ADDINEN.CITE<EndNote><Cite><Author>Carreira</Author><Year>2017</Year><RecNum>123</RecNum><DisplayText><styleface="superscript">[8]</style></DisplayText><record><rec-number>123</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618402477">123</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Carreira,J.</author><author>Zisserman,A.</author></authors></contributors><titles><title>QuoVadis,ActionRecognition?ANewModelandtheKineticsDataset</title><secondary-title>2017IEEEConferenceonComputerVisionandPatternRecognition(CVPR)</secondary-title></titles><dates><year>2017</year></dates><urls></urls></record></Cite></EndNote>[\o"Carreira,2017#123"8]提出I3D网络，通过在三维卷积网络基础上增加网络宽度的方式提高网络分类性能，在UCF101数据集上仅取RGB情况下其准确率为84.5%，通过在kinetics数据集预训练参数进行优化最终结果达到98%，然而文章使用64个GPU并行训练，其网络复杂及较大计算量较难在现实场景下应用。针对传统的三维卷积缺乏空间及通道依赖性问题，2020年王辉涛ADDINEN.CITE<EndNote><Cite><Author>王辉涛</Author><Year>2020</Year><RecNum>124</RecNum><DisplayText><styleface="superscript">[9]</style></DisplayText><record><rec-number>124</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618403172">124</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>王辉涛</author><author>胡燕</author></authors></contributors><auth-address>武汉理工大学计算机学院;</auth-address><titles><title>基于全局时空感受野的高效视频分类方法%J小型微型计算机系统</title></titles><pages>1768-1775</pages><volume>41</volume><number>08</number><keywords><keyword>视频分类</keyword><keyword>卷积神经网络</keyword><keyword>通道和空间注意力</keyword><keyword>全局时空感受野</keyword><keyword>三维卷积核分解</keyword></keywords><dates><year>2020</year></dates><isbn>1000-1220</isbn><call-num>21-1106/TP</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"王辉涛,2020#124"9]提出了首先将传统的三维卷积核分解成空域卷积核和时域卷积核，然后在二维网络引入通道和空间的方法，在UCF101数据集准确率最高为96.6%，虽然在一定程度上通过放弃准确度来提升速度，然而由于将3维卷积核拆分和多网络拼接，难以进行快速实时分类。在改进3D卷积网络方面：2017年杨曙光ADDINEN.CITE<EndNote><Cite><Author>现代计算机</Author><Year>2017</Year><RecNum>125</RecNum><DisplayText><styleface="superscript">[10]</style></DisplayText><record><rec-number>125</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618403460">125</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>杨曙光%J现代计算机</author></authors></contributors><titles><title>一种改进的深度学习视频分类方法</title></titles><pages>68-71</pages><number>08</number><dates><year>2017</year></dates><urls></urls></record></Cite><Cite><Author>现代计算机</Author><Year>2017</Year><RecNum>125</RecNum><record><rec-number>125</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618403460">125</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>杨曙光%J现代计算机</author></authors></contributors><titles><title>一种改进的深度学习视频分类方法</title></titles><pages>68-71</pages><number>08</number><dates><year>2017</year></dates><urls></urls></record></Cite></EndNote>[\o"现代计算机,2017#125"10]为解决精度下降问题，将3D卷积方法提取的短视频特征放入LSTM进行序列识别，在UCF101数据集上分别进行了5类、30类、101类的视频分类试验，准确率分别为80%、63%和43%。相较于单一图片特征，2021年张丽娟、井佩光ADDINEN.CITE<EndNote><Cite><Author>张丽娟</Author><RecNum>126</RecNum><DisplayText><styleface="superscript">[11]</style></DisplayText><record><rec-number>126</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618403760">126</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>张丽娟</author><author>崔天舒</author><author>井佩光</author><author>苏育挺</author></authors></contributors><auth-address>天津大学电气自动化与信息工程学院;</auth-address><titles><title>基于深度多模态特征融合的短视频分类%J北京航空航天大学学报</title></titles><pages>1-9</pages><keywords><keyword>短视频</keyword><keyword>多模态学习</keyword><keyword>深度网络</keyword><keyword>分类</keyword><keyword>特征空间</keyword></keywords><dates></dates><isbn>1001-5965</isbn><call-num>11-2625/V</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"张丽娟,#126"11]等人基于音频模态采用相似性损失函数提取特征进行视频分类。2021年陈意ADDINEN.CITE<EndNote><Cite><Author>陈意</Author><Year>2021</Year><RecNum>127</RecNum><DisplayText><styleface="superscript">[12]</style></DisplayText><record><rec-number>127</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618403871">127</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>陈意</author><author>黄山</author></authors></contributors><auth-address>四川大学电气工程学院;</auth-address><titles><title>基于改进NeXtVLAD的视频分类%J计算机工程与设计</title></titles><pages>749-754</pages><volume>42</volume><number>03</number><keywords><keyword>深度学习</keyword><keyword>视频分类</keyword><keyword>局部聚合描述子向量</keyword><keyword>特征融合</keyword><keyword>卷积神经网络</keyword></keywords><dates><year>2021</year></dates><isbn>1000-7024</isbn><call-num>11-1775/TP</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"陈意,2021#127"12]等人基于改进NeXtVLAD在VideoNet-100数据集中分类准确率最高为85.4%。在细分类领域，2020年李钊光ADDINEN.CITE<EndNote><Cite><Author>电子测量技术</Author><Year>2020</Year><RecNum>128</RecNum><DisplayText><styleface="superscript">[13]</style></DisplayText><record><rec-number>128</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618403939">128</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>李钊光%J电子测量技术</author></authors></contributors><titles><title>基于深度学习和迁移学习的体育视频分类研究</title></titles><pages>27-31</pages><volume>v.43;No.350</volume><number>18</number><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>[\o"电子测量技术,2020#128"13]针对体育视频提出了基于对VGG-16模型迁移学习大方法，对其自行采集的10和15类体育视频数据集进行分类，准确率分别达到94%和92%，然而仅对体育视频分类并不能满足需求。2视频关键帧选取研究现状视频关键帧是将视频变成视频帧的形式，并通过选取若干视频帧作为整个视频信息代表。在一定程度上减少了视频的冗余度，用较小的内存空间和较少的信息特征表示较复杂的信息内容。将较为成熟的图像研究方法迁移至视频研究中，视频关键帧选取是目前对视频进行深入研究的关键处理步骤，目前视频关键帧选取的方法可分为：(1)镜头检测法。一种常用的方法是选取每个镜头中的第一帧选为关键帧ADDINEN.CITE<EndNote><Cite><Author>Cernekova</Author><Year>2005</Year><RecNum>135</RecNum><DisplayText><styleface="superscript">[14]</style></DisplayText><record><rec-number>135</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618485942">135</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Cernekova,Z.</author><author>Pitas,I.</author><author>Nikou,C.%JIEEETransactionsonCircuits</author><author>SystemsforVideoTechnology</author></authors></contributors><titles><title>Informationtheory-basedshotcut/fadedetectionandvideosummarization</title></titles><pages>82-91</pages><volume>16</volume><number>1</number><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>[\o"Cernekova,2005#135"14]，刘政凯等人ADDINEN.CITE<EndNote><Cite><Author>刘政凯</Author><Year>2002</Year><RecNum>136</RecNum><DisplayText><styleface="superscript">[15]</style></DisplayText><record><rec-number>136</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618486714">136</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>刘政凯</author><author>汤晓鸥%J计算机工程与应用</author></authors></contributors><titles><title>视频检索中镜头分割方法综述</title></titles><pages>84-87</pages><number>23</number><dates><year>2002</year></dates><urls></urls></record></Cite></EndNote>[\o"刘政凯,2002#136"15]先将视频分成若干个相邻视频帧特征图无明显变化的镜头，再从每个镜头中提取关键帧。2019年梁建胜ADDINEN.CITE<EndNote><Cite><Author>梁建胜</Author><Year>2019</Year><RecNum>153</RecNum><DisplayText><styleface="superscript">[16]</style></DisplayText><record><rec-number>153</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618923677">153</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>梁建胜</author><author>温贺平%J控制工程</author></authors></contributors><titles><title>基于深度学习的视频关键帧提取与视频检索</title></titles><pages>965-970</pages><volume>026</volume><number>005</number><dates><year>2019</year></dates><urls></urls></record></Cite></EndNote>[\o"梁建胜,2019#153"16]设计一种自适应的关键帧选择算法，在小波变换后，度量小波变换的距离并将其保存为向量，识别并提取视频中每个镜头的摘要信息，选取包含最多显著特征的帧作为该镜头的关键帧。KucuktuncOADDINEN.CITE<EndNote><Cite><Author>Kuecuektunc</Author><Year>2010</Year><RecNum>155</RecNum><DisplayText><styleface="superscript">[17]</style></DisplayText><record><rec-number>155</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618924274">155</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Kuecuektunc,Onur</author><author>Gueduekbay,Ugur</author><author>Ulusoy,Oezguer%JComputerVision</author><author>ImageUnderstanding</author></authors></contributors><titles><title>Fuzzycolorhistogram-basedvideosegmentation</title></titles><pages>125-134</pages><volume>114</volume><number>1</number><dates><year>2010</year></dates><urls></urls></record></Cite></EndNote>[\o"Kuecuektunc,2010#155"17]提出一种基于模糊颜色直方图的镜头分割方法，除了检测突变、渐变镜头，还可以对掉帧、加字幕、加图标等较难识别视频帧进行检测。镜头检测法具有计算简单且易于实现的特点，然而提取的关键帧一般为镜头突变或渐变处的视频帧，既无法处理关键帧冗余问题，又无法充分描述视频内容。(2)特征提取法。在对视频帧图像提取如颜色、亮度、纹理等底层特征后，计算相邻两帧图像特征之间的差值，通过比较特征差值是否大于预设阈值来确定是否为关键帧，ZhangADDINEN.CITE<EndNote><Cite><Author>Hong</Author><Year>1997</Year><RecNum>137</RecNum><DisplayText><styleface="superscript">[18]</style></DisplayText><record><rec-number>137</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618487742">137</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Hong</author><author>Jiang</author><author>Zhang</author><author>and</author><author>Jianhua</author><author>Wu</author><author>and</author><author>Di</author><author>Zhong</author><author>and%JPatternRecognition</author></authors></contributors><titles><title>Anintegratedsystemforcontent-basedvideoretrievalandbrowsing</title></titles><dates><year>1997</year></dates><urls></urls></record></Cite></EndNote>[\o"Hong,1997#137"18]和GunselADDINEN.CITE<EndNote><Cite><Author>Gunsel</Author><Year>2002</Year><RecNum>138</RecNum><DisplayText><styleface="superscript">[19]</style></DisplayText><record><rec-number>138</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618487815">138</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Gunsel,B.</author><author>Tekalp,A.M.</author></authors></contributors><titles><title>Content-basedvideoabstraction</title><secondary-title>InternationalConferenceonImageProcessing</secondary-title></titles><dates><year>2002</year></dates><urls></urls></record></Cite></EndNote>[\o"Gunsel,2002#138"19]等人提出了基于颜色直方图的方法。因为要对视频中的每幅图像进行一种或多种特征的提取，所以计算量比较大。(3)运动分析法。首先将视频分成若干个镜头，然后通过光流法计算每个镜头的运动量，当运动量取局部最小值时，选择该镜头中对应的帧作为最终的关键帧。FanLADDINEN.CITE<EndNote><Cite><Author>Fan</Author><Year>2013</Year><RecNum>154</RecNum><DisplayText><styleface="superscript">[20]</style></DisplayText><record><rec-number>154</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618923852">154</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Fan,L.</author><author>Wu,Q.</author><author>Ruan,C.</author><author>Zhuo,Z.</author><author>Wang,X.%JIEEProceedings-VisionImage</author><author>SignalProcessing</author></authors></contributors><titles><title>Afeatureextractionalgorithmbasedon2Dcomplexityofgaborwaveletstransformforfacialexpressionrecognition</title></titles><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>[\o"Fan,2013#154"20]采用运动向量特征作为视频关键帧特征，由于特征提取时间长，故不适用于大规模的视频相似性检测。(4)聚类选取法。结合了图像全局或局部特征，利用特定的相似度或相异度将处理对象进行分组，具有相似特征划分到同一个簇中，再从各簇中选取关键帧。Wu等人ADDINEN.CITE<EndNote><Cite><Author>Wu</Author><Year>2017</Year><RecNum>140</RecNum><DisplayText><styleface="superscript">[21]</style></DisplayText><record><rec-number>140</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618489014">140</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>JiaxinWu</author><author>Sheng-huaZhong</author><author>JianminJiang</author><author>YunyunYang</author></authors></contributors><auth-address>ShenzhenUniversity;;ShenzhenUniversity;;ShenzhenUniversity;;HarbinInstituteofTechnologyShenzhenGraduateSchool</auth-address><titles><title>Anovelclusteringmethodforstaticvideosummarization%JMultimediaToolsandApplications</title></titles><volume>76</volume><number>7</number><keywords><keyword>Staticvideosummarization</keyword><keyword>Clusteringmethod</keyword><keyword>Videorepresentation</keyword></keywords><dates><year>2017</year></dates><isbn>1380-7501</isbn><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"Wu,2017#140"21]先利用特征向量的奇异值分解来进行预采样，减少给定视频的冗余信息，再用词袋模型来表示候选帧的视觉内容，最后利用基于视频表示的高密度峰值聚类算法选取关键帧。吴先宇ADDINEN.CITE<EndNote><Cite><Author>吴先宇</Author><Year>2018</Year><RecNum>152</RecNum><DisplayText><styleface="superscript">[22]</style></DisplayText><record><rec-number>152</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618923341">152</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>吴先宇</author><author>王之杰</author><author>石蕴玉</author><author>吴寅腾</author><author>张一鸣</author><author>程凯亮%J福建电脑</author></authors></contributors><titles><title>广告视频关键帧提取软件的设计与开发</title></titles><pages>130-131+177</pages><volume>v.34</volume><number>04</number><dates><year>2018</year></dates><urls></urls></record></Cite></EndNote>[\o"吴先宇,2018#152"22]针对广告视频通过舍弃过亮过暗的视频帧，结合均值哈希算法、聚类查重，将一段广告视频浓缩为十几或几十张图像。3视频相似性判定及检测算法研究现状(1)视频相似性判定视频相似性检测包括重复视频检测、近似重复视频ADDINEN.CITE<EndNote><Cite><Author>Wu</Author><Year>2007</Year><RecNum>158</RecNum><DisplayText><styleface="superscript">[23]</style></DisplayText><record><rec-number>158</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618968978">158</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Wu,X.</author><author>Hauptmann,A.G.</author><author>Ngo,C.W.</author><author>Wu,P.</author></authors></contributors><titles><title>Practicaleliminationofnear-duplicatesfromwebvideosearch</title><secondary-title>InternationalconferenceonMultimedia</secondary-title></titles><dates><year>2007</year></dates><urls></urls></record></Cite></EndNote>[\o"Wu,2007#158"23]检测和拷贝视频检测。重复视频即为几乎一模一样的视频，范围较窄；近似重复视频，要求语义一致、画面近似，视频来源一般不同；而拷贝视频，要求语义一致、画面近似且视频来源相同ADDINEN.CITE<EndNote><Cite><Author>顾佳伟</Author><Year>2017</Year><RecNum>161</RecNum><DisplayText><styleface="superscript">[24]</style></DisplayText><record><rec-number>161</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618971857">161</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>顾佳伟</author><author>赵瑞玮</author><author>姜育刚%J计算机研究与发展</author></authors></contributors><titles><title>视频拷贝检测方法综述</title></titles><pages>1238-1250</pages><volume>54</volume><number>006</number><dates><year>2017</year></dates><urls></urls></record></Cite></EndNote>[\o"顾佳伟,2017#161"24]。例如，学生用各自的手机分别记录同一时间段内教师授课状态及授课内容，这2个视频视为近似重复视频而不是拷贝视频；如果一名同学对另一名同学拍摄的视频进行后期加工，如加入字幕或贴纸元素，则新视频才被视为拷贝视频。早期认为拷贝检测与近似重复检测有明显的差异，后来，Basharat等人ADDINEN.CITE<EndNote><Cite><Author>Basharat</Author><Year>2008</Year><RecNum>159</RecNum><DisplayText><styleface="superscript">[25]</style></DisplayText><record><rec-number>159</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618969197">159</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Basharat,A.</author><author>Yun,Z.</author><author>Shah,M.%JComputerVision</author><author>ImageUnderstanding</author></authors></contributors><titles><title>Contentbasedvideomatchingusingspatiotemporalvolumes</title></titles><pages>360-377</pages><volume>110</volume><number>3</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>[\o"Basharat,2008#159"25]建议放宽定义，本文视频相似性检测包括如上视频检测，包括在文件格式、编码参数、光度变化(颜色、光线变化)、编辑操作(标题、logo和边框插入)、不同长度和某些修改(帧添加/删除)方面的不同，用户会清楚地识别出这些视频“本质上是相同的”。针对视频相似性检测所使用的检测方法，不同研究者选择的方法存在共通性ADDINEN.CITE<EndNote><Cite><Author>Liu</Author><Year>2013</Year><RecNum>160</RecNum><DisplayText><styleface="superscript">[26]</style></DisplayText><record><rec-number>160</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618971185">160</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Liu,J.</author><author>Huang,Z.I.</author><author>Cai,H.</author><author>Shen,H.T.</author><author>Chong,W.N.</author><author>Wang,W.%JIEEEMultimedia</author></authors></contributors><titles><title>Near-DuplicateVideoRetrieval:CurrentResearchandFutureTrends</title></titles><pages>1-1</pages><volume>45</volume><number>4</number><dates><year>2013</year></dates><urls></urls></record></Cite></EndNote>[\o"Liu,2013#160"26]。(2)视频指纹提取视频指纹是指基于视频内容形成的签名，可以专门用来表示某一个视频ADDINEN.CITE<EndNote><Cite><Author>Kim</Author><Year>2014</Year><RecNum>133</RecNum><DisplayText><styleface="superscript">[27]</style></DisplayText><record><rec-number>133</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618484899">133</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>SeminKim</author><author>SeungHoLee</author><author>YongManRo</author></authors></contributors><auth-address>ImageandVideoSystemsLab,Dept.ofElectricalEngineering,KoreaAdvancedInstituteofScienceandTechnology(KAIST),Yuseong-Gu,Daejeon305-701,RepublicofKorea</auth-address><titles><title>Rotationandflippingrobustregionbinarypatternsforvideocopydetection%JJournalofVisualCommunicationandImageRepresentation</title></titles><volume>25</volume><number>2</number><keywords><keyword>Regionbinarypattern</keyword><keyword>Rotationrobustpattern</keyword><keyword>Flippingrobustpattern</keyword><keyword>Videocopydetection</keyword><keyword>Videofingerprint</keyword><keyword>Localbinarypattern</keyword><keyword>ImageDescriptor</keyword><keyword>VideoDescriptor</keyword></keywords><dates><year>2014</year></dates><isbn>1047-3203</isbn><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"Kim,2014#133"27]。要在视频数据库中查找某个视频的相似视频，可以在相应的指纹数据库中搜索其指纹并进行匹配。两个指纹的紧密性代表了相应视频之间的相似性，两个感知上不同的视频应该有不同的指纹ADDINEN.CITE<EndNote><Cite><Author>Zahedi</Author><Year>2015</Year><RecNum>134</RecNum><DisplayText><styleface="superscript">[28]</style></DisplayText><record><rec-number>134</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618485046">134</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Zahedi,M.</author><author>Ghadi,O.R.%JSignalImage</author><author>VideoProcessing</author></authors></contributors><titles><title>CombiningGaborfilterandFFTforfingerprintenhancementbasedonaregionaladaptionmethodandautomaticsegmentation</title></titles><pages>267-275</pages><volume>9</volume><number>2</number><dates><year>2015</year></dates><urls></urls></record></Cite></EndNote>[\o"Zahedi,2015#134"28]。视频指纹提取可以分为三种：基于空域提取视频指纹、基于时域提取视频指纹和基于时空结合生成视频指纹。1)基于空域提取视频指纹。以视频的图像特征为基础提取视频指纹，如形状、颜色、纹理等特征作为视频的唯一身份证明。E.MADDINEN.CITE<EndNote><Cite><Author>Maani</Author><Year>2008</Year><RecNum>141</RecNum><DisplayText><styleface="superscript">[29]</style></DisplayText><record><rec-number>141</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618493082">141</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Maani,E.</author><author>Tsaftaris,S.A.</author><author>Katsaggelos,A.K.%JIEEE</author></authors></contributors><titles><title>Localfeatureextractionforvideocopydetectioninadatabase</title></titles><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>[\o"Maani,2008#141"29]基于帧运动强度差异提取关键帧，以一个矢量来描述K.MADDINEN.CITE<EndNote><Cite><Author>Mikolajczyk</Author><Year>2005</Year><RecNum>142</RecNum><DisplayText><styleface="superscript">[30]</style></DisplayText><record><rec-number>142</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618493164">142</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Mikolajczyk,K.</author><author>Schmid,C.%JIEEETransactionsonPatternAnalysis</author><author>MachineIntelligence</author></authors></contributors><titles><title>APerformanceEvaluationofLocalDescriptors</title></titles><pages>1615-1630</pages><volume>27</volume><number>10</number><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>[\o"Mikolajczyk,2005#142"30]提取的兴趣点，使用DP技术降低计算复杂度，对低比特率压缩鲁棒性较好。A.SarADDINEN.CITE<EndNote><Cite><Author>Sarkar</Author><Year>2008</Year><RecNum>143</RecNum><DisplayText><styleface="superscript">[31]</style></DisplayText><record><rec-number>143</rec-number><foreign-keys><keyapp="EN"db-id="59px5dpxe0e0dpe2fs6ps5xgetrpwxvf2p0w"timestamp="1618493382">143</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Sarkar,A.</author><author>Ghosh,P.</author><author>Moxley,E.</author><author>Manjunath,B.S.</author></authors></contributors><titles><title>VideoFingerprinting:FeaturesforDuplicateandSimilarVideoDetectionandQuery-basedVideoRetrieval</title><secondary-title>MultimediaContentAccess:AlgorithmsandSystemsII</secondary-title></titles><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>[\o"Sarkar,2008#143"31]提取CFMT(CompactFourier-MellinTransform)、SIFT、亮度色度直方图和有序直方图等特征，指出CFMT用于复制视频检测时效果比SIFT好，但相似视频检索时SIFT比CFMT效果更佳。2）基于时域提取视频指纹。以运动特征为基础提取视频指纹，如亮度变化当作能辨别视频身份的证明。LiChenADDINEN.CITE<EndNote><Cite><Author>Chen</Author><Year>2008</Year>