Multimodal Analysis of User-Generated Multimedia Content - Couverture souple
Livre 6 sur 10: Socio-Affective Computing
Synopsis
1 Introduction
1.1 Background and Motivation
1.2 Overview
1.3 Acronyms and Notations
1.4 Roadmap
2 Literature Review
2.1 Event Understanding
2.2 Tag Recommendation and Ranking
2.3 Soundtrack Recommendation for UGVs
2.4 Lecture Video Segmentation
3 Event Understanding
3.1 Introduction
3.2 System Overview3.2.1 EventBuilder
3.2.2 EventSensor
3.3 Evaluation
3.3.1 EventBuilder
3.3.2 EventSensor
3.4 Summary
4 Tag Recommendation and Ranking
4.1 Introduction
4.1.1 Tag Recommendation
4.1.2 Tag Ranking
4.2 System Overview
4.2.1 Tag Recommendation4.2.2 Random Walk based Relevance Scores
4.2.3 Fusion of Different Tag Recommendation Approaches
4.2.4 Tag Ranking
4.3 Evaluation
4.3.1 Tag Recommendation
4.3.2 Tag Ranking
4.4 Summary
5 Soundtrack Recommendation for UGVs
5.1 Introduction
5.1.1 Increasing Popularity of User-Generated Videos
5.1.2 Challenges with User-Generated Videos in Viewing and Sharing
5.1.3 Motivation for Generating Music Videos for Outdoor User-Generated Videos
5.2 Music Video Generation
5.2.1 Scene Moods Prediction Models
5.2.2 Music Retrieval Techniques
5.2.3 Automatic Music Video Generation Model
5.3 Evaluation
5.3.1 Dataset and Experimental Settings
5.3.2 Evaluation Metrics
5.3.3 Objective Evaluation
5.3.4 Subjective Evaluation
5.3.5 Experimental Results
5.3.6 Comparison with State-of-the-arts
5.3.7 Discussion of Results
5.4 Summary
6 Lecture Video Segmentation
6.1 Introduction
6.2 Lecture Video Segmentation
6.2.1 Prediction of Video Transition Cues using Supervised Learning
6.2.2 Computation of Text Transition Cues using N-gram based Language Model
6.2.3 Computation of SRT Segment Boundaries using the state-of-the-art
6.2.4 Computation of Wikipedia Segment Boundaries
6.2.5 Transition File Generation
<6.3 Evaluation6.3.1 Dataset and Experimental Settings
6.3.2 Results from the ATLAS System
6.3.3 Results from the TRACE System
6.4 Summary
7 Conclusions and future work
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