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Table of Contents
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
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