Learning Cross-Domain Information Transfer for Location Recognition and Clustering

Estimating geographic location from images is a challenging problem that is receiving recent attention. In contrast to many existing methods that primarily model discriminative information corresponding to different locations, we propose joint learning of information that images across locations share and vary upon. Starting with generative and discriminative subspaces pertaining to domains, which are obtained by a hierarchical grouping of images from adjacent locations, we present a top-down approach that first models cross-domain information transfer by utilizing the geometry of these subspaces, and then encodes the model results onto individual images to infer their location. We report competitive results for location recognition and clustering on two public datasets, im2GPS and San Francisco, and empirically validate the utility of various design choices involved in the approach.

[1]  K.A. Gallivan,et al.  Efficient algorithms for inferences on Grassmann manifolds , 2004, IEEE Workshop on Statistical Signal Processing, 2003.

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

[3]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Alexei A. Efros,et al.  What makes Paris look like Paris? , 2015, Commun. ACM.

[5]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[7]  Yang Song,et al.  Tour the world: Building a web-scale landmark recognition engine , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Alan Edelman,et al.  The Geometry of Algorithms with Orthogonality Constraints , 1998, SIAM J. Matrix Anal. Appl..

[9]  Rama Chellappa,et al.  Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[11]  Marc Pollefeys,et al.  Leveraging 3D City Models for Rotation Invariant Place-of-Interest Recognition , 2011, International Journal of Computer Vision.

[12]  Jan-Michael Frahm,et al.  From structure-from-motion point clouds to fast location recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Anas Al-Nuaimi,et al.  Mobile Visual Location Recognition , 2013 .

[14]  Xin Chen,et al.  City-scale landmark identification on mobile devices , 2011, CVPR 2011.

[15]  Daniel D. Lee,et al.  Grassmann discriminant analysis: a unifying view on subspace-based learning , 2008, ICML '08.

[16]  Zhen Li,et al.  A Comparative Study of Mobile-Based Landmark Recognition Techniques , 2010, IEEE Intelligent Systems.

[17]  Huizhong Chen,et al.  Mobile visual search using image and text features , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[18]  Wen Gao,et al.  Location Discriminative Vocabulary Coding for Mobile Landmark Search , 2012, International Journal of Computer Vision.

[19]  Jan-Michael Frahm,et al.  Visual word based location recognition in 3D models using distance augmented weighting , 2008 .

[20]  Rama Chellappa,et al.  Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.

[21]  Bir Bhanu,et al.  Landmark recognition for autonomous mobile robots , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[22]  Martial Hebert,et al.  Evaluation of Image-Based Landmark Recognition Techniques , 1998 .

[23]  Larry S. Davis,et al.  A Robust and Scalable Approach to Face Identification , 2010, ECCV.

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

[25]  Antonio Criminisi,et al.  Epitomic Location Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Daniel P. Huttenlocher,et al.  Location Recognition Using Prioritized Feature Matching , 2010, ECCV.

[27]  Y. Chikuse.  Statistics on special manifolds , 2003 .

[28]  Gang Hua,et al.  Joint People, Event, and Location Recognition in Personal Photo Collections Using Cross-Domain Context , 2010, ECCV.

[29]  Ankita Kumar,et al.  Experiments on visual loop closing using vocabulary trees , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[30]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[31]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[32]  Jan-Michael Frahm,et al.  Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs , 2008, ECCV.

[33]  Tomás Pajdla,et al.  Avoiding Confusing Features in Place Recognition , 2010, ECCV.