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Issue No.12 - December (2009 vol.31)
pp: 2158-2167
Anitha Kannan , Microsoft Research Search Labs, Mountain View
Antonio Criminisi , Microsoft Research Cambridge, Cambridge
John Winn , Microsoft Research Cambridge, Cambridge
This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions, and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yields enhanced generalization with economical training. Experiments on both existing and new labeled image databases result in recognition accuracy superior to state of the art with real-time computational performance.
Location class recognition, epitomic image analysis, panoramic stitching.
Anitha Kannan, Antonio Criminisi, John Winn, "Epitomic Location Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 12, pp. 2158-2167, December 2009, doi:10.1109/TPAMI.2009.165
[1] L. Breiman, “Random Forests,” Technical Report TR567, Univ. of California Berkley, 1999.
[2] M. Brown and D. Lowe, “Automatic Panoramic Image Stitching Using Invariant Features,” Int'l J. Computer Vision, vol. 74, pp. 59-73, 2007.
[3] A. Criminisi, J. Shotton, A. Blake, C. Rother, and P.H.S. Torr, “Efficient Dense Stereo with Occlusions by Four-State Dynamic Programming,” Int'l J. Computer Vision, 2006.
[4] A.J. Davison, “Real-Time Simultaneous Localisation and Mapping with a Single Camera,” Proc. Int'l Conf. Computer Vision, pp. 1403-1410, 2003.
[5] B. Johansson and R. Cipolla, “A System for Automatic Pose-Estimation from a Single Image in a City Scene,” Proc. Int'l Assoc. of Science and Technology for Development Int'l Conf. Signal Processing, Pattern Recognition and Applications, 2002.
[6] N. Jojic, B. Frey, and A. Kannan, “Epitomic Analysis of Appearance and Shape,” Proc. Int'l Conf. Computer Vision, 2003.
[7] D.G. Lowe, “Object Recognition from Local Scale-Invariant Features,” Proc. Int'l Conf. Computer Vision, pp. 1150-1157, 1999.
[8] A. Oliva and A. Torralba, “Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope,” Int'l J. Computer Vision, vol. 42, no. 3, pp. 145-175, 2001.
[9] N. Petrovic, A. Ivanovic, N. Jojic, S. Basu, and T. Huang, “Recursive Estimation of Generative Models of Video,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 79-86, 2006.
[10] D. Robertson and R. Cipolla, “An Image Based System for Urban Navigation,” Proc. British Machine Vision Conf., 2004.
[11] F. Schaffalitzky and A. Zisserman, “Multi-View Matching for Unordered Image Sets, or ‘How Do I Organize My Holiday Snaps?’” Proc. European Conf. Computer Vision, pp. 414-431, 2002.
[12] G. Schindler, M. Brown, and R. Szeliski, “City-Scale Location Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[13] S. Se, D. Lowe, and J. Little, “Vision-Based Mobile Robot Localization and Mapping Using Scale-Invariant Features,” Proc. IEEE Int'l Conf. Robotics and Automation, pp. 2051-2058, 2001.
[14] N. Snavely, S.M. Seitz, and R. Szeliski, “Photo Tourism: Exploring Photo Collections in 3D,” Proc. ACM SIGGRAPH, pp. 835-846, 2006.
[15] A. Torralba, K.P. Murphy, W.T. Freeman, and M.A. Rubin, “Context-Based Vision System for Place and Object Recognition,” Proc. Int'l Conf. Computer Vision, vol. 1, pp. 273-280, 2003.
[16] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[17] J. Wang, H. Zha, and R. Cipolla, “Coarse-to-Fine Vision-Based Localization by Indexing Scale-Invariant Features,” IEEE Trans. Systems, Man, and Cybernetics—Part B, vol. 36, no. 2, pp. 413-422, Apr. 2006.
[18] B. Williams, G. Klein, and I. Reid, “Real-Time Slam Relocalisation,” Proc. Int'l Conf. Computer Vision, 2007.
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