Different Pattern Recognition in Re-Ranking Process Using Image Based Retrieval
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Full Text(PDF, 3000) PP.
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Author(s) |
Ravi Kumar Kallakunta, Dr. Balaji Savadam, Mahidhar Chowdary Mullapudi |
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KEYWORDS |
mobile visual Search, image-based retrieval, geometric verification, robust features
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ABSTRACT |
We propose a location geometric similarity scoring method that is invariant to rotation, scale, and translation, and can be easily incorporated in mobile visual search and augmented reality systems. We present a fast and efficient geometric re-ranking method that can be incorporated in a feature based image-based retrieval system that utilizes a Vocabulary Tree (VT). We form feature pairs by comparing descriptor classification paths in the VT and calculate geometric similarity score of these pairs. We compare the performance of the location geometric scoring scheme to orientation and scale geometric scoring schemes. We show in our experiments that re-ranking schemes can substantially improve recognition accuracy. We can also reduce the worst case server latency up to 1 sec and still improve the recognition performance
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References |
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[1] G. Takacs, V. Chandrasekhar, N. Gelfand, Y. Xiong, W. Chen, T. Bismpigiannis,
R. Grzeszczuk, K. Pulli, and B. Girod, “Outdoors augmented reality on mobile
phone using loxel-based visual feature organization,” in ACM International
Conference on Multimedia Information Retrieval, Vancouver, Canada, October
2008.
[2] S. S. Tsai, D. Chen, J. Singh, and B. Girod, “Rate-efficient, real-time CD cover
recognition on a camera-phone,” in ACM International Conference on Multimedia,
Vancouver, Canada, October 2008.
[3] D. Chen, S. S. Tsai, R. Vedantham, R. Grzeszczuk, and B. Girod, “Streaming
mobile augmented reality on mobile phones,” in International Symposium on
Mixed and Augmented Reality, Orlando, FL, USA, October 2009.
[4] J. Sivic and A. Zisserman, “Video google: a text retrieval approach to object
matching in videos,” in International Conference on Computer Vision, 2003, vol.
2, pp. 1470–1477.
[5] J. Philbin, O. Chum, M Isard, J. Sivic, and A. Zisserman, “Object retrieval with
large vocabularies and fast spatial matching,” in Conference on Computer Vision
and Pattern Recognition, 2007, pp. 1–8.
[6] G. Schindler, M. Brown, and R. Szeliski, “City-scale location recognition,” in
Conference on Computer Vision and Pattern Recognition, New York, NY, USA,
June 2007, pp. 1–7.
[7] D. Lowe, “Distinctive image features from scale-invariant keypoints,” International
Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, November 2004.
[8] H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: speeded up robust features,” in
European Conference on Computer Vision, Graz, Austria, May 2006, pp. 404–
417.
[9] V. Chandrasekhar, G. Takacs, D. Chen, S. S. Tsai, R. Grzeszczuk, and B. Girod,
“CHoG: Compressed Histogram of Gradients,” in In Proceedings of Conference
on Computer Vision and Pattern Recognition, 2009.
[10] D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in
Conference on Computer Vision and Pattern Recognition, New York, NY, USA,
June 2006, pp. 2161–2168.
[11] M. Fischler and R. Bolles, “Random sample consensus: a paradigm for model
fitting with applications to image analysis and automated cryptography,”
Communications of ACM, vol. 24, no. 1, pp. 381–395, 1981.
[12] O. Chum, J. Matas, and J. V. Kittler, “Locally optimized RANSAC,” in Proceedings
of DAGM, 2003, pp. 236–243.
[13] O. Chum, T. Werner, and J. Matas, “Epipolar geometry estimation via ransac
benefits from the oriented epipolar constraint,” in International Conference on
Pattern Recognition, Washington, DC, USA, 2004, pp. 112–115, IEEE Computeriety.
[14] H. Jegou, M. Douze, and C. Schmid, “Hamming embedding and weak geometric
consistency for large scale image search,” in European Conference on
Computer Vision, 2008, pp. I: 304–317.
[15] Z. Wu, Q. Ke, M. Isard, and J. Sun, “Bundling features for large scale partialduplicate
web image search,” in Conference on Computer Vision and Pattern
Recognition, 2009, pp. 25–32.
[16] O. Chum, M. Perdoch, and J. Matas, “Geometric min-hashing: Finding a
(thick) needle in a haystack,” in Conference on Computer Vision and Pattern
Recognition. 2009, pp. 17–24, IEEE.
[17] G. Takacs, V. Chandrasekhar, S. S. Tsai, D. M. Chen, R. Vedantham, R.
Grzeszczuk, and B. Girod, “Unified real-time tracking and recognition with
rotation-invariant fast features,” in Conference on Computer Vision and Pattern
Recognition, 2010, p. submitted.
[18] S. S. Tsai, D. M. Chen, G. Takacs, V. Chandrasekhar, R. Vedantham, R.
Grzeszczuk, and B. Girod, “Location coding for mobile image retrieval,” in Proc.
5th International Mobile Multimedia Communications Conference, 2009.
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