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Table of Contents
Preface; Acknowledgments; Contents; Contributors; 1 Introduction; References; 2 The Benchmark as a Research Catalyst: Charting the Progress of Geo-prediction for Social Multimedia; 2.1 Introduction; 2.1.1 The Placing Challenge for Social Multimedia; 2.1.2 The Benefits of Benchmarking; 2.2 Charting the Progress; 2.2.1 Placing Task 2010: Inception; 2.2.2 Placing Task 2011: Consolidation; 2.2.3 Placing Task 2012: Expansion; 2.2.4 Placing Task 2013: Volume; 2.2.5 Placing Task 2014: Horizons; 2.3 Future Challenges for Geoprediction of Social Multimedia
2.3.1 Further Development of Definitions of ``Place''2.3.2 Definitions of the Task of Placing Social Multimedia; 2.4 Conclusion and Outlook; 2.4.1 Where Placing has Been; 2.4.2 Where Placing Is Going; References; 3 Large-Scale Image Geolocalization; 3.1 Introduction; 3.1.1 Background; 3.1.2 Chapter Outline; 3.2 Building a Geo-tagged Image Dataset; 3.2.1 Evaluation Test Set; 3.3 Simple, Baseline Geolocalization Method; 3.3.1 Is the Data Helping?; 3.3.2 Grouping Geolocation Estimates; 3.4 Improving Geolocalization with More Features and Lazy Learning
3.4.1 Geometry Specific Color and Texton Histograms3.4.2 Bags of SIFT Features; 3.4.3 Geolocalization with Additional Features; 3.4.4 Lazy Learning for Large-Scale Scene Geolocalization; 3.4.5 Geolocalization Results with New Features and Lazy Learning; 3.5 Why Does it Work? Deeper Performance Analysis; 3.5.1 Measuring Performance Without Geographic Bias.; 3.5.2 Measuring Category Level Geolocation Performance.; 3.5.3 Measuring Landmark Geolocation Performance; 3.6 Discussion; References; 4 Vision-Based Fine-Grained Location Estimation; 4.1 Landmark and Location Recognition
4.2 Image-Based Location Recognition4.3 Estimating the Camera Viewing Direction; 4.4 City-Scale Location Recognition; 4.4.1 Large-Scale Image Database Indexing; 4.4.2 Informative Codebook Generation; 4.4.3 Geo-Visual Clutering; 4.5 Location Estimation by 2D
3D Alignment; 4.5.1 3D Model Reconstruction; 4.5.2 Image Localization by View Registration; 4.6 Accurate Mobile Visual Localization and Its Applications; 4.6.1 Aerial-Imagery Matching; 4.6.2 Intensity-Based Matching Through Dynamic Time Warping; 4.6.3 Conclusions; References
5 Image-Based Positioning of Mobile Devices in Indoor Environments5.1 Introduction; 5.2 Database Preparation; 5.3 Image Retrieval and Pose Estimation; 5.4 Confidence Estimation; 5.5 Experimental Results; 5.6 Conclusion; References; 6 Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments; 6.1 Introduction; 6.2 Approaching the City-Verification Task; 6.2.1 MFCC Acoustic Feature Extraction; 6.2.2 Gaussian Mixture Modeling; 6.2.3 GMM-SVM Approach; 6.2.4 Language Modeling; 6.2.5 Performance Evaluation; 6.3 Related Work; 6.4 Dataset
2.3.1 Further Development of Definitions of ``Place''2.3.2 Definitions of the Task of Placing Social Multimedia; 2.4 Conclusion and Outlook; 2.4.1 Where Placing has Been; 2.4.2 Where Placing Is Going; References; 3 Large-Scale Image Geolocalization; 3.1 Introduction; 3.1.1 Background; 3.1.2 Chapter Outline; 3.2 Building a Geo-tagged Image Dataset; 3.2.1 Evaluation Test Set; 3.3 Simple, Baseline Geolocalization Method; 3.3.1 Is the Data Helping?; 3.3.2 Grouping Geolocation Estimates; 3.4 Improving Geolocalization with More Features and Lazy Learning
3.4.1 Geometry Specific Color and Texton Histograms3.4.2 Bags of SIFT Features; 3.4.3 Geolocalization with Additional Features; 3.4.4 Lazy Learning for Large-Scale Scene Geolocalization; 3.4.5 Geolocalization Results with New Features and Lazy Learning; 3.5 Why Does it Work? Deeper Performance Analysis; 3.5.1 Measuring Performance Without Geographic Bias.; 3.5.2 Measuring Category Level Geolocation Performance.; 3.5.3 Measuring Landmark Geolocation Performance; 3.6 Discussion; References; 4 Vision-Based Fine-Grained Location Estimation; 4.1 Landmark and Location Recognition
4.2 Image-Based Location Recognition4.3 Estimating the Camera Viewing Direction; 4.4 City-Scale Location Recognition; 4.4.1 Large-Scale Image Database Indexing; 4.4.2 Informative Codebook Generation; 4.4.3 Geo-Visual Clutering; 4.5 Location Estimation by 2D
3D Alignment; 4.5.1 3D Model Reconstruction; 4.5.2 Image Localization by View Registration; 4.6 Accurate Mobile Visual Localization and Its Applications; 4.6.1 Aerial-Imagery Matching; 4.6.2 Intensity-Based Matching Through Dynamic Time Warping; 4.6.3 Conclusions; References
5 Image-Based Positioning of Mobile Devices in Indoor Environments5.1 Introduction; 5.2 Database Preparation; 5.3 Image Retrieval and Pose Estimation; 5.4 Confidence Estimation; 5.5 Experimental Results; 5.6 Conclusion; References; 6 Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments; 6.1 Introduction; 6.2 Approaching the City-Verification Task; 6.2.1 MFCC Acoustic Feature Extraction; 6.2.2 Gaussian Mixture Modeling; 6.2.3 GMM-SVM Approach; 6.2.4 Language Modeling; 6.2.5 Performance Evaluation; 6.3 Related Work; 6.4 Dataset