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基于深度学习的视频关键帧提取与视频检索.docx

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基于深度学习的视频关键帧提取与视频检索 Title:DeepLearning-basedVideoKeyframeExtractionandVideoRetrieval Abstract: Videoanalysisandretrievalarecriticaltasksinmultimediaapplications.Keyframeextractionplaysacrucialroleinsummarizingvideoseffectivelyandefficiently.Inaddition,videoretrievalsystemsrelyonaccurateandinformativekeyframestoprovideuserswithbettersearchresults.Withtherecentadvancementsindeeplearningtechniques,ithasbecomefeasibletoleveragetheirpowerforkeyframeextractionandvideoretrievaltasks.Thispaperexplorestheapplicationofdeeplearninginvideokeyframeextractionandvideoretrieval,providinganoverviewofrelevantresearch,techniques,andchallenges. 1.Introduction(150words) Videosareapopularformofmediathatgeneratetremendousamountsofdata.Consequently,theneedforefficientvideoanalysisandretrievalhasbecomeincreasinglyimportantinvariousdomainssuchassurveillance,entertainment,andeducation.Videokeyframeextraction,astheprocessofselectingrepresentativeframesfromavideosequence,playsavitalroleinvideosummarization.Deeplearning,withitsabilitytolearnhierarchicalrepresentationsfromvastamountsofdata,offersapromisingapproachtoautomaticallyextractrelevantandinformativekeyframes.Moreover,videoretrievalsystemscanleveragedeeplearningmodelstoenhancesearchaccuracybymatchingqueryframeswithkeyframes. 2.VideoKeyframeExtraction(300words) 2.1TraditionalMethods 2.2DeepLearningApproaches 2.2.1ConvolutionalNeuralNetworks(CNN) 2.2.2RecurrentNeuralNetworks(RNN) 2.2.3GenerativeAdversarialNetworks(GAN) 2.2.4One-shotLearningModels 2.3EvaluationMetrics 3.VideoRetrieval(300words) 3.1TraditionalApproaches 3.2DeepLearning-basedRetrievalMethods 3.2.1ConvolutionalNeuralNetworks(CNN)forFeatureExtraction 3.2.2SiameseNetworksforSimilarityMatching 3.2.3RecurrentNeuralNetworks(RNN)forTemporalUnderstanding 3.2.4AttentionMechanismsforVideoContextUnderstanding 3.3EvaluationMetrics 4.ChallengesandFutureDirections(300words) 4.1DatasetandAnnotationChallenges 4.2GeneralizationandScalability 4.3Real-timeProcessingandEfficiency 4.4EthicalConside