Content and context information fusion for mobile landmark recognition

This paper proposes a new information fusion approach that employs two information components for mobile landmark recognition, which includes: content analysis and context analysis. Existing landmark recognition works are mainly based on PC platform, which uses content analysis alone for recognition, and thus has a large computation cost and cannot satisfy mobile users' fast response time requirements. Therefore in this paper, a mobile platform-based landmark recognition system is developed. The bag-of-words method is employed and further improved as content analysis according to the specific features of landmark images. Context analysis based on location (obtained by global positioning system) and direction (obtained by digital compass) information is then integrated with improved BoW as a new information fusion approach for mobile landmark recognition. Finally, the experiment results conducted on the NTU landmark database show that the proposed approach is effective for mobile landmark recognition.

[1]  Tao Chen,et al.  Integrated Content and Context Analysis for Mobile Landmark Recognition , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  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..

[3]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 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, J. Field Robotics.

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

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

[7]  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).

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

[9]  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).

[10]  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).

[11]  Tao Chen,et al.  From universal bag-of-words to adaptive bag-of-phrases for mobile scene recognition , 2011, 2011 18th IEEE International Conference on Image Processing.

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