Large-Scale Visual Geo-Localization

This timely and authoritative volume explores the bidirectional relationship between images and locations. The text presents a comprehensive review of the state of the art in large-scale visual geo-localization, and discusses the emerging trends in this area. Valuable insights are supplied by a pre-eminent selection of experts in the field, into a varied range of real-world applications of geo-localization. Topics and features: discusses the latest methods to exploit internet-scale image databases for devising geographically rich features and geo-localizing query images at different scales; investigates geo-localization techniques that are built upon high-level and semantic cues; describes methods that perform precise localization by geometrically aligning the query image against a 3D model; reviews techniques that accomplish image understanding assisted by the geo-location, as well as several approaches for geo-localization under practical, real-world settings.

[1]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[2]  Sunil Arya,et al.  Approximate nearest neighbor queries in fixed dimensions , 1993, SODA '93.

[3]  Richard Szeliski,et al.  Geometrically Constrained Structure from Motion: Points on Planes , 1998, SMILE.

[4]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[5]  Koby Crammer,et al.  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..

[6]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[9]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[10]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[11]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[12]  Eli Shechtman,et al.  Matching Local Self-Similarities across Images and Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Richard Szeliski,et al.  City-Scale Location Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Antonio Criminisi,et al.  Harvesting Image Databases from the Web , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Trevor Darrell,et al.  Autotagging Facebook: Social network context improves photo annotation , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Fei-Fei Li,et al.  Towards Scalable Dataset Construction: An Active Learning Approach , 2008, ECCV.

[17]  Alexei A. Efros,et al.  IM2GPS: estimating geographic information from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[19]  Daniel P. Huttenlocher,et al.  Landmark classification in large-scale image collections , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Jiebo Luo,et al.  Geotagging in multimedia and computer vision—a survey , 2010, Multimedia Tools and Applications.

[22]  Mubarak Shah,et al.  Accurate Image Localization Based on Google Maps Street View , 2010, ECCV.

[23]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

[24]  Alexei A. Efros,et al.  Data-driven visual similarity for cross-domain image matching , 2011, ACM Trans. Graph..

[25]  Andrew Owens,et al.  Discrete-continuous optimization for large-scale structure from motion , 2011, CVPR 2011.

[26]  Bastian Leibe,et al.  Visual Object Recognition , 2011, Visual Object Recognition.

[27]  Jan-Michael Frahm,et al.  Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs , 2008, International Journal of Computer Vision.

[28]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[29]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..

[30]  Jure Leskovec,et al.  Image Labeling on a Network: Using Social-Network Metadata for Image Classification , 2012, ECCV.

[31]  Feng Wu,et al.  3D visual phrases for landmark recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  Jan-Michael Frahm,et al.  Improved Geometric Verification for Large Scale Landmark Image Collections , 2012, BMVC.

[34]  Masatoshi Okutomi,et al.  Visual Place Recognition with Repetitive Structures , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Claudia Hauff,et al.  A study on the accuracy of Flickr's geotag data , 2013, SIGIR.

[36]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[37]  Christian Szegedy,et al.  DeepPose: Human Pose Estimation via Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  R. Fergus,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[40]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[41]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Stefan Lee,et al.  Predicting Geo-informative Attributes in Large-Scale Image Collections Using Convolutional Neural Networks , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[44]  Pascal Fua,et al.  Worldwide Pose Estimation Using 3D Point Clouds , 2012, ECCV.