Multimodal Analysis of User-Generated Multimedia Content (eBook)

Rajiv Ratn Shah received his B.Sc. with honors in Mathematics from Banaras Hindu University, India in 2005. He received his M.Tech. in Computer Technology and Applications from Delhi Technological University, India in 2010. Prior joining Indraprastha Institute of Information Technology Delhi (IIIT Delhi), India as an assistant professor, Dr Shah has received his Ph.D. in Computer Science from the National University of Singapore, Singapore. Currently, he is also working as a research fellow in living analytics research centre (LARC) at the Singapore Management University, Singapore. His research interests include the multimodal analysis of user-generated multimedia content in the support of social media applications, multimodal event detection and recommendation, and multimedia analysis, search, and retrieval. Dr Shah is the recipient of several awards, including the runner-up in the Grand Challenge competition of ACM International Conference on Multimedia. He is involved in reviewing of many top-tier international conferences and journals. He has published several research work in top-tier conferences and journals such as Springer MultiMedia Modeling, ACM International Conference on Multimedia, IEEE International Symposium on Multimedia, and Elsevier Knowledge-Based Systems.

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 6.3.1 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