Multimodal location estimation of videos and images. ([2014])
- Record Type:
- Book
- Title:
- Multimodal location estimation of videos and images. ([2014])
- Main Title:
- Multimodal location estimation of videos and images
- Further Information:
- Note: Jaeyoung Choi, Gerald Friedland, editor.
- Editors:
- Choi, Jaeyoung
Friedland, Gerald, 1978- - 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 CategoryPreface; 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. … (more)
- Publisher Details:
- Cham : Springer
- Publication Date:
- 2014
- Copyright Date:
- 2015
- Extent:
- 1 online resource (xii, 191 pages), color illustrations
- Subjects:
- 006.6
Engineering
Location-based services
Image processing -- Digital techniques
Multimedia systems
COMPUTERS -- General
Image processing -- Digital techniques
Location-based services
Multimedia systems
Engineering & Applied Sciences
Electrical & Computer Engineering
Electrical Engineering
Applied Physics
Telecommunications
Technology & Engineering -- Telecommunications
Computers -- Interactive & Multimedia
Communications engineering / telecommunications
Graphical & digital media applications
Telecommunication
Technology & Engineering -- Electronics -- General
Imaging systems & technology
Electronic books - Languages:
- English
- ISBNs:
- 9783319098616
3319098616
3319098608
9783319098609 - Related ISBNs:
- 9783319098609
- Notes:
- Note: Online resource; title from PDF title page (SpringerLink, viewed October 15, 2014).
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.371425
- Ingest File:
- 01_356.xml