A Comparative Study of Mobile-Based Landmark Recognition Techniques

Mobile-based landmark recognition is becoming increasingly appealing due to the proliferation of mobile devices coupled with improving processing techniques, imaging capability, and networking infrastructure. This article provides a general overview of existing mobile-based and nonmobile-based landmark recognition systems and their differences. We discuss content and context analysis and compare landmark classification methods. We also present the experimental results of our own mobile landmark recognition evaluations based on content analysis, context analysis, and integrated content-context analysis.

[1]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[2]  Andrew Zisserman,et al.  Scene Classification Using a Hybrid Generative/Discriminative Approach , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Aly A. Farag,et al.  CSIFT: A SIFT Descriptor with Color Invariant Characteristics , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Pat Langley,et al.  Place recognition in dynamic environments , 1997 .

[5]  Joo-Hwee Lim,et al.  Outdoor place recognition using compact local descriptors and multiple queries with user verification , 2007, ACM Multimedia.

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

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

[8]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Gertjan J. Burghouts,et al.  Performance evaluation of local colour invariants , 2009, Comput. Vis. Image Underst..

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

[11]  Xing Xie,et al.  Photo-to-search: using multimodal queries to search the web from mobile devices , 2005, MIR '05.

[12]  Long Quan,et al.  Image-based tree modeling , 2007, SIGGRAPH 2007.

[13]  Konrad Tollmar,et al.  Searching the Web with mobile images for location recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Alexei A. Efros,et al.  Scene completion using millions of photographs , 2007, SIGGRAPH 2007.

[15]  Gregory Dudek,et al.  Robust place recognition using local appearance based methods , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[16]  Lucas Paletta,et al.  A Mobile Vision System for Urban Detection with Informative Local Descriptors , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[17]  Joo-Hwee Lim,et al.  Object identification and retrieval from efficient image matching. Snap2Tell with the STOIC dataset , 2007, Inf. Process. Manag..

[18]  Tao Chen,et al.  A multi-scale learning approach for landmark recognition using mobile devices , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[19]  Joo-Hwee Lim,et al.  Object Identification and Retrieval from Efficient Image Matching: Snap2Tell with the STOIC Dataset , 2005, AIRS.

[20]  Joo-Hwee Lim,et al.  Scene Recognition with Camera Phones for Tourist Information Access , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[21]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[22]  Richard Szeliski,et al.  Multi-image matching using multi-scale oriented patches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).