Multimodal Analysis of User-Generated Multimedia Content

by Rajiv Shah

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 Overview 3.2.1 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 Recommendation 4.2.2 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 Evaluation 6.3.1 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

288 pages, Paperback

Published September 7, 2017