http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Multimodal Analysis of User-Generated Multimedia Content
1 online resource (xxii, 263 pages 63 illustrations, 42 illustrations in color)
This book presents a study of semantics and sentics understanding derived from user-generated multimodal content (UGC). It enables researchers to learn about the ways multimodal analysis of UGC can augment semantics and sentics understanding and it helps in addressing several multimedia analytics problems from social media such as event detection and summarization, tag recommendation and ranking, soundtrack recommendation, lecture video segmentation, and news video uploading. Readers will discover how the derived knowledge structures from multimodal information are beneficial for efficient multimedia search, retrieval, and recommendation. However, real-world UGC is complex, and extracting the semantics and sentics from only multimedia content is very difficult because suitable concepts may be exhibited in different representations. Moreover, due to the increasing popularity of social media websites and advancements in technology, it is now possible to collect a significant amount of important contextual information (e.g., spatial, temporal, and preferential information). Thus, there is a need to analyze the information of UGC from multiple modalities to address these problems. A discussion of multimodal analysis is presented followed by studies on how multimodal information is exploited to address problems that have a significant impact on different areas of society (e.g., entertainment, education, and journalism). Specifically, the methods presented exploit the multimedia content (e.g., visual content) and associated contextual information (e.g., geo-, temporal, and other sensory data). The reader is introduced to several knowledge bases and fusion techniques to address these problems. This work includes future directions for several interesting multimedia analytics problems that have the potential to significantly impact society. The work is aimed at researchers in the multimedia field who would like to pursue research in the area of multimodal analysis of UGC.
Dedication; Foreword; Preface; Acknowledgements; Contents; About the Authors; Abbreviations; Chapter 1: Introduction; 1.1 Background and Motivation; 1.2 Overview; 1.2.1 Event Understanding; 1.2.2 Tag Recommendation and Ranking; 1.2.3 Soundtrack Recommendation for UGVs; 1.2.4 Automatic Lecture Video Segmentation; 1.2.5 Adaptive News Video Uploading; 1.3 Contributions; 1.3.1 Event Understanding; 1.3.2 Tag Recommendation and Ranking; 1.3.3 Soundtrack Recommendation for UGVs; 1.3.4 Automatic Lecture Video Segmentation; 1.3.5 Adaptive News Video Uploading; 1.4 Knowledge Bases and APIs.
1.4.1 FourSquare1.4.2 Semantics Parser; 1.4.3 SenticNet; 1.4.4 WordNet; 1.4.5 Stanford POS Tagger; 1.4.6 Wikipedia; 1.5 Roadmap; References; Chapter 2: Literature Review; 2.1 Event Understanding; 2.2 Tag Recommendation and Ranking; 2.3 Soundtrack Recommendation for UGVs; 2.4 Lecture Video Segmentation; 2.5 Adaptive News Video Uploading; References; Chapter 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; References; Chapter 4: Tag Recommendation and Ranking.
4.1 Introduction4.1.1 Tag Recommendation; 4.1.2 Tag Ranking; 4.2 System Overview; 4.2.1 Tag Recommendation; 4.2.2 Tag Ranking; 4.3 Evaluation; 4.3.1 Tag Recommendation; 4.3.2 Tag Ranking; 4.4 Summary; References; Chapter 5: Soundtrack Recommendation for UGVs; 5.1 Introduction; 5.2 Music Video Generation; 5.2.1 Scene Moods Prediction Models; 5.2.1.1 Geo and Visual Features; 5.2.1.2 Scene Moods Classification Model; 5.2.1.3 Scene Moods Recognition; 5.2.2 Music Retrieval Techniques; 5.2.2.1 Heuristic Method for Soundtrack Retrieval; 5.2.2.2 Post-Filtering with User Preferences.
5.2.3 Automatic Music Video Generation Model5.3 Evaluation; 5.3.1 Dataset and Experimental Settings; 5.3.1.1 Emotion Tag Space; 5.3.1.2 GeoVid Dataset; 5.3.1.3 Soundtrack Dataset; 5.3.1.4 Evaluation Dataset; 5.3.2 Experimental Results; 5.3.2.1 Scene Moods Prediction Accuracy; 5.3.2.2 Soundtrack Selection Accuracy; 5.3.3 User Study; 5.4 Summary; References; Chapter 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 -Gram Based Language Model.
6.2.2.1 Preparation6.2.2.2 Title/Sub-Title Text Extraction; 6.2.2.3 Transition Time Recommendation from SRT File; 6.2.3 Computation of SRT Segment Boundaries Using a Linguistic-Based Approach; 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; References; Chapter 7: Adaptive News Video Uploading; 7.1 Introduction; 7.2 Adaptive News Video Uploading; 7.2.1 NEWSMAN Scheduling Algorithm; 7.2.2 Rate-Distortion (R-D) Model.
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Includes bibliographical references and index.