SlideShare une entreprise Scribd logo
1  sur  20
Near-Duplicate Video Detection UsingTemporal Patterns of Semantic Concepts,[object Object],IEEE International Symposium on Multimedia,[object Object],San Diego, California, USADecember 14-16, 2009,[object Object],Hyun-seok Min, Jaeyoung Choi, Wesley De Neve, Yong Man Ro,[object Object],Image and Video Systems Lab,[object Object],Department of Electrical Engineering,[object Object],Korea Advanced Institute of Science and Technology (KAIST),[object Object],Daejeon, Republic of Korea,[object Object]
Overview,[object Object],Introduction,[object Object],Near-duplicates,[object Object],Semantic video signatures,[object Object],Experimental results,[object Object],Conclusions,[object Object],2 /20,[object Object]
Introduction,[object Object],Importance of duplicate video detection,[object Object],prevents cluttering of search results,[object Object],prevents copyright infringement,[object Object],3 /20,[object Object],Search results for the query “I will survive Jesus”,[object Object],A significant number of search results are near-duplicates!,[object Object]
Definition of Near-duplicates,[object Object],Identicalor approximately identical videos,[object Object],photometric variations,[object Object],e.g., change of color and lighting,[object Object],editing operations,[object Object],e.g., insertion of captions, logos, and borders,[object Object],speed changes,[object Object],e.g., addition or removal of frames,[object Object],semantic concepts,[object Object],e.g., ‘road’, ‘sand’, ‘snow’ , ….,[object Object],4 /20,[object Object]
Examples of Near-duplicates,[object Object],original videos,[object Object],near-duplicates,[object Object],transformation,[object Object],(cam cording, insertion of subtitles),[object Object],5 /20,[object Object],transformation,[object Object],(blur),[object Object]
Video Signatures,[object Object],[object Object]
represents a video segment with a unique set of features
Conventional video signatures
often created by extracting low-level visual features fromvideo frames6 /20,[object Object],video content,[object Object],featureextraction,[object Object],video signature,[object Object],…,[object Object]
Use of Low-level Visual Features forCreating a Video Signature,[object Object],Problem,[object Object],near-duplicates may not be visually similar,[object Object],original video,[object Object],near-duplicate,[object Object],transformation,[object Object],(cam cording, insertion of subtitles),[object Object],Visual match? No!,[object Object],video signature,[object Object],video signature,[object Object],…,[object Object],…,[object Object],7 /20,[object Object]
Semantic Similarity,[object Object],Observation,[object Object],near-duplicates often contain similar semantics,[object Object],original video,[object Object],near-duplicate,[object Object],transformation,[object Object],(cam cording, insertion of subtitles),[object Object],Semantic match? Yes!,[object Object],Semantic concepts:,[object Object],Semantic concepts:,[object Object],indoor, man, face, …,[object Object],indoor, man, face, …,[object Object],8 /20,[object Object]
Use of Semantic Concepts forCreating a Video signature,[object Object],Semantic concept detection,[object Object],traditionally used for classifying video clips into several predefined concepts,[object Object],Problem,[object Object],limited number of semantic concepts can be detected,[object Object],Solution,[object Object],use of temporal variation of semantic concepts,[object Object],different from video sequence to video sequence,[object Object],9 /20,[object Object]
Semantic Video Signature Creation (1/2),[object Object],Semantic video signature creation,[object Object],A1,[object Object],A2,[object Object],A3,[object Object],…,[object Object],…,[object Object],Semantic video signature,[object Object],V,[object Object],video,[object Object],shots,[object Object],key frames,[object Object],…,[object Object],concept classification,[object Object],classifier for ‘Street’,[object Object],classifier for ‘Beach’,[object Object],classifier for ‘Tree’,[object Object],Ai,[object Object],AN,[object Object],N: the number of shots,[object Object],M: the number of predefined semantics,[object Object],Ci: ith predefined semantic concept,[object Object],semantic video signature,[object Object],si,[object Object],…,[object Object],sN,[object Object],s2,[object Object],s1,[object Object],…,[object Object],10 /20,[object Object]
Semantic Video Signature Creation (2/2),[object Object],11 /20,[object Object],original video,[object Object],near-duplicate,[object Object],transformation,[object Object],…,[object Object],…,[object Object],…,[object Object],…,[object Object],Semantic video signature of original video,[object Object],Semantic video signature of near-duplicate,[object Object]
Matching Procedure,[object Object],12 /20,[object Object],Semantic video signature of near-duplicate,[object Object],Semantic video signature of original video,[object Object]
Experimental Setup (1/3),[object Object],Reference database,[object Object],video sequences taken from TRECVID2007,[object Object],over 9 hours of video data,[object Object],format: MPEG-1,[object Object],resolution: 352X288,[object Object],frame rate: 25 frame per second (fps),[object Object],Screenshots,[object Object],13 /20,[object Object]
Experimental Setup (2/3),[object Object],Creation of query video (near-duplicate) set ,[object Object],number of query video sequences,[object Object],64 in total,[object Object],average length of the query video sequences,[object Object],3 minutes,[object Object],Process for generating query video sequences ,[object Object],original video,[object Object],sampling,[object Object],subvideoof original video,[object Object],transformation,[object Object],query video,[object Object],14 /20,[object Object]
Experimental Setup (3/3),[object Object],Transformations used,[object Object],spatial transformations,[object Object],Gaussian blur,[object Object],logo insertion ,[object Object],letter-box,[object Object],resizing,[object Object],temporal transformations,[object Object],change of frame rate,[object Object],original,[object Object],15 /20,[object Object]
Experimental Results: Spatial Transformation (1/2),[object Object],16 /20,[object Object],The precision increases as the threshold value decreases, while in turn, the recall value decreases.,[object Object],blur,[object Object],letter-box,[object Object]
Experimental Results: Spatial Transformation (2/2),[object Object],17 /20,[object Object],Ordinal measurement does not work well with logo insertion, compared to the proposed method.,[object Object]

Contenu connexe

Similaire à Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts

Op Sy 03 Ch 71
Op Sy 03 Ch 71Op Sy 03 Ch 71
Op Sy 03 Ch 71 Google
 
Video compression
Video compressionVideo compression
Video compressionDarkNight14
 
Digital Video
Digital VideoDigital Video
Digital VideoVideoguy
 
Towards Using Semantic Features for Near-Duplicate Video Detection
Towards Using Semantic Features for Near-Duplicate Video DetectionTowards Using Semantic Features for Near-Duplicate Video Detection
Towards Using Semantic Features for Near-Duplicate Video DetectionWesley De Neve
 
Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...
Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...
Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...FIAT/IFTA
 
Applying Media Content Analysis to the Production of Musical Videos as Summar...
Applying Media Content Analysis to the Production of Musical Videos as Summar...Applying Media Content Analysis to the Production of Musical Videos as Summar...
Applying Media Content Analysis to the Production of Musical Videos as Summar...Chris Huang
 
Cycle-Contrast for Self-Supervised Video Represenation Learning
Cycle-Contrast for Self-Supervised Video Represenation LearningCycle-Contrast for Self-Supervised Video Represenation Learning
Cycle-Contrast for Self-Supervised Video Represenation LearningQuan Kong
 
Flexible Transport of 3D Videos over Networks
Flexible Transport of 3D Videos over NetworksFlexible Transport of 3D Videos over Networks
Flexible Transport of 3D Videos over NetworksAhmed Hamza
 
Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...
Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...
Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...Wanjin Yu
 
01_Introduction.pdf.pdf
01_Introduction.pdf.pdf01_Introduction.pdf.pdf
01_Introduction.pdf.pdfWidedMiled2
 
Encoding stored video for stremming applications ieee paper ppt
Encoding stored video for stremming applications ieee paper pptEncoding stored video for stremming applications ieee paper ppt
Encoding stored video for stremming applications ieee paper pptNavin Kumar
 
1 state of-the-art and trends in scalable video
1 state of-the-art and trends in scalable video1 state of-the-art and trends in scalable video
1 state of-the-art and trends in scalable videoYogananda Patnaik
 
Real-Time Video Copy Detection in Big Data
Real-Time Video Copy Detection in Big DataReal-Time Video Copy Detection in Big Data
Real-Time Video Copy Detection in Big DataIRJET Journal
 
How to make a video - Part 2: Post-Production Basics
How to make a video - Part 2: Post-Production BasicsHow to make a video - Part 2: Post-Production Basics
How to make a video - Part 2: Post-Production BasicsKris Brewer
 
Ac02417471753
Ac02417471753Ac02417471753
Ac02417471753IJMER
 

Similaire à Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts (20)

Op Sy 03 Ch 71
Op Sy 03 Ch 71Op Sy 03 Ch 71
Op Sy 03 Ch 71
 
Video compression
Video compressionVideo compression
Video compression
 
Digital Video
Digital VideoDigital Video
Digital Video
 
Towards Using Semantic Features for Near-Duplicate Video Detection
Towards Using Semantic Features for Near-Duplicate Video DetectionTowards Using Semantic Features for Near-Duplicate Video Detection
Towards Using Semantic Features for Near-Duplicate Video Detection
 
Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...
Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...
Are you Digitized Files Really OK? Levels of QC and Film Digitization (SCHALL...
 
Applying Media Content Analysis to the Production of Musical Videos as Summar...
Applying Media Content Analysis to the Production of Musical Videos as Summar...Applying Media Content Analysis to the Production of Musical Videos as Summar...
Applying Media Content Analysis to the Production of Musical Videos as Summar...
 
AcademicProject
AcademicProjectAcademicProject
AcademicProject
 
Cycle-Contrast for Self-Supervised Video Represenation Learning
Cycle-Contrast for Self-Supervised Video Represenation LearningCycle-Contrast for Self-Supervised Video Represenation Learning
Cycle-Contrast for Self-Supervised Video Represenation Learning
 
Flexible Transport of 3D Videos over Networks
Flexible Transport of 3D Videos over NetworksFlexible Transport of 3D Videos over Networks
Flexible Transport of 3D Videos over Networks
 
Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...
Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...
Human Behavior Understanding: From Human-Oriented Analysis to Action Recognit...
 
01_Introduction.pdf.pdf
01_Introduction.pdf.pdf01_Introduction.pdf.pdf
01_Introduction.pdf.pdf
 
Digital video
Digital videoDigital video
Digital video
 
Encoding stored video for stremming applications ieee paper ppt
Encoding stored video for stremming applications ieee paper pptEncoding stored video for stremming applications ieee paper ppt
Encoding stored video for stremming applications ieee paper ppt
 
1 state of-the-art and trends in scalable video
1 state of-the-art and trends in scalable video1 state of-the-art and trends in scalable video
1 state of-the-art and trends in scalable video
 
Real-Time Video Copy Detection in Big Data
Real-Time Video Copy Detection in Big DataReal-Time Video Copy Detection in Big Data
Real-Time Video Copy Detection in Big Data
 
Multi media unit-3.doc
Multi media unit-3.docMulti media unit-3.doc
Multi media unit-3.doc
 
Mm video
Mm videoMm video
Mm video
 
How to make a video - Part 2: Post-Production Basics
How to make a video - Part 2: Post-Production BasicsHow to make a video - Part 2: Post-Production Basics
How to make a video - Part 2: Post-Production Basics
 
Ac02417471753
Ac02417471753Ac02417471753
Ac02417471753
 
Mpeg7
Mpeg7Mpeg7
Mpeg7
 

Plus de Wesley De Neve

Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...Wesley De Neve
 
Investigating the biological relevance in trained embedding representations o...
Investigating the biological relevance in trained embedding representations o...Investigating the biological relevance in trained embedding representations o...
Investigating the biological relevance in trained embedding representations o...Wesley De Neve
 
Impact of adversarial examples on deep learning models for biomedical image s...
Impact of adversarial examples on deep learning models for biomedical image s...Impact of adversarial examples on deep learning models for biomedical image s...
Impact of adversarial examples on deep learning models for biomedical image s...Wesley De Neve
 
Learning Biologically Relevant Features Using Convolutional Neural Networks f...
Learning Biologically Relevant Features Using Convolutional Neural Networks f...Learning Biologically Relevant Features Using Convolutional Neural Networks f...
Learning Biologically Relevant Features Using Convolutional Neural Networks f...Wesley De Neve
 
The 5th Aslla Symposium
The 5th Aslla SymposiumThe 5th Aslla Symposium
The 5th Aslla SymposiumWesley De Neve
 
Ghent University Global Campus 101
Ghent University Global Campus 101Ghent University Global Campus 101
Ghent University Global Campus 101Wesley De Neve
 
Booklet for the First GUGC Research Symposium
Booklet for the First GUGC Research SymposiumBooklet for the First GUGC Research Symposium
Booklet for the First GUGC Research SymposiumWesley De Neve
 
Center for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global CampusCenter for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global CampusWesley De Neve
 
Center for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global CampusCenter for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global CampusWesley De Neve
 
Learning biologically relevant features using convolutional neural networks f...
Learning biologically relevant features using convolutional neural networks f...Learning biologically relevant features using convolutional neural networks f...
Learning biologically relevant features using convolutional neural networks f...Wesley De Neve
 
Towards reading genomic data using deep learning-driven NLP techniques
Towards reading genomic data using deep learning-driven NLP techniquesTowards reading genomic data using deep learning-driven NLP techniques
Towards reading genomic data using deep learning-driven NLP techniquesWesley De Neve
 
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
 
GUGC Info Session - Informatics and Bioinformatics
GUGC Info Session - Informatics and BioinformaticsGUGC Info Session - Informatics and Bioinformatics
GUGC Info Session - Informatics and BioinformaticsWesley De Neve
 
Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...
Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...
Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...Wesley De Neve
 
Ghent University and GUGC-K: Overview of Teaching and Research Activities
Ghent University and GUGC-K: Overview of Teaching and Research ActivitiesGhent University and GUGC-K: Overview of Teaching and Research Activities
Ghent University and GUGC-K: Overview of Teaching and Research ActivitiesWesley De Neve
 
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...Wesley De Neve
 
Exploring Deep Machine Learning for Automatic Right Whale Recognition and No...
 Exploring Deep Machine Learning for Automatic Right Whale Recognition and No... Exploring Deep Machine Learning for Automatic Right Whale Recognition and No...
Exploring Deep Machine Learning for Automatic Right Whale Recognition and No...Wesley De Neve
 
Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...
Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...
Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...Wesley De Neve
 
Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...
Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...
Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...Wesley De Neve
 
Towards Twitter hashtag recommendation using distributed word representations...
Towards Twitter hashtag recommendation using distributed word representations...Towards Twitter hashtag recommendation using distributed word representations...
Towards Twitter hashtag recommendation using distributed word representations...Wesley De Neve
 

Plus de Wesley De Neve (20)

Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...
 
Investigating the biological relevance in trained embedding representations o...
Investigating the biological relevance in trained embedding representations o...Investigating the biological relevance in trained embedding representations o...
Investigating the biological relevance in trained embedding representations o...
 
Impact of adversarial examples on deep learning models for biomedical image s...
Impact of adversarial examples on deep learning models for biomedical image s...Impact of adversarial examples on deep learning models for biomedical image s...
Impact of adversarial examples on deep learning models for biomedical image s...
 
Learning Biologically Relevant Features Using Convolutional Neural Networks f...
Learning Biologically Relevant Features Using Convolutional Neural Networks f...Learning Biologically Relevant Features Using Convolutional Neural Networks f...
Learning Biologically Relevant Features Using Convolutional Neural Networks f...
 
The 5th Aslla Symposium
The 5th Aslla SymposiumThe 5th Aslla Symposium
The 5th Aslla Symposium
 
Ghent University Global Campus 101
Ghent University Global Campus 101Ghent University Global Campus 101
Ghent University Global Campus 101
 
Booklet for the First GUGC Research Symposium
Booklet for the First GUGC Research SymposiumBooklet for the First GUGC Research Symposium
Booklet for the First GUGC Research Symposium
 
Center for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global CampusCenter for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global Campus
 
Center for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global CampusCenter for Biotech Data Science at Ghent University Global Campus
Center for Biotech Data Science at Ghent University Global Campus
 
Learning biologically relevant features using convolutional neural networks f...
Learning biologically relevant features using convolutional neural networks f...Learning biologically relevant features using convolutional neural networks f...
Learning biologically relevant features using convolutional neural networks f...
 
Towards reading genomic data using deep learning-driven NLP techniques
Towards reading genomic data using deep learning-driven NLP techniquesTowards reading genomic data using deep learning-driven NLP techniques
Towards reading genomic data using deep learning-driven NLP techniques
 
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...
 
GUGC Info Session - Informatics and Bioinformatics
GUGC Info Session - Informatics and BioinformaticsGUGC Info Session - Informatics and Bioinformatics
GUGC Info Session - Informatics and Bioinformatics
 
Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...
Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...
Ghent University Global Campus - Sungkyunkwan University: Workshop on Researc...
 
Ghent University and GUGC-K: Overview of Teaching and Research Activities
Ghent University and GUGC-K: Overview of Teaching and Research ActivitiesGhent University and GUGC-K: Overview of Teaching and Research Activities
Ghent University and GUGC-K: Overview of Teaching and Research Activities
 
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...
 
Exploring Deep Machine Learning for Automatic Right Whale Recognition and No...
 Exploring Deep Machine Learning for Automatic Right Whale Recognition and No... Exploring Deep Machine Learning for Automatic Right Whale Recognition and No...
Exploring Deep Machine Learning for Automatic Right Whale Recognition and No...
 
Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...
Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...
Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expe...
 
Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...
Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...
Multimedia Lab @ Ghent University - iMinds - Organizational Overview & Outlin...
 
Towards Twitter hashtag recommendation using distributed word representations...
Towards Twitter hashtag recommendation using distributed word representations...Towards Twitter hashtag recommendation using distributed word representations...
Towards Twitter hashtag recommendation using distributed word representations...
 

Dernier

IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfJamie (Taka) Wang
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureEric D. Schabell
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 
9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding TeamAdam Moalla
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintMahmoud Rabie
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPathCommunity
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7DianaGray10
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
20230202 - Introduction to tis-py
20230202 - Introduction to tis-py20230202 - Introduction to tis-py
20230202 - Introduction to tis-pyJamie (Taka) Wang
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 

Dernier (20)

IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
20230104 - machine vision
20230104 - machine vision20230104 - machine vision
20230104 - machine vision
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability Adventure
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and Istio
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 
201610817 - edge part1
201610817 - edge part1201610817 - edge part1
201610817 - edge part1
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership Blueprint
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation Developers
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
20230202 - Introduction to tis-py
20230202 - Introduction to tis-py20230202 - Introduction to tis-py
20230202 - Introduction to tis-py
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 

Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts