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Issue No.03 - July-September (2011 vol.18)
pp: 86-94
Bernd Girod , Stanford University
Vijay Chandrasekhar , Stanford University
Radek Grzeszczuk , Nokia Research Center
Yuriy A. Reznik , Qualcomm
Editor's Note: Sophisticated visual-search and augmented-reality applications are beginning to emerge on mobile platforms. Such applications impose unique requirements on latency, processing, and bandwidth, and also demand robust recognition performance. This article describes a new standard that is being developed to address the functionality and interoperability needs for mobile visual-search applications.
visual search, augmented reality, mobile platforms, visual-search, IEEE MultiMedia, Industry and Standards, graphics and multimedia
Bernd Girod, Vijay Chandrasekhar, Radek Grzeszczuk, Yuriy A. Reznik, "Mobile Visual Search: Architectures, Technologies, and the Emerging MPEG Standard", IEEE MultiMedia, vol.18, no. 3, pp. 86-94, July-September 2011, doi:10.1109/MMUL.2011.48
1. J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," Proc. IEEE Int'l Conf. Computer Vision (ICCV), IEEE Press, 2003.
2. D. Nister and H. Stewenius, "Scalable Recognition with a Vocabulary Tree," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), IEEE Press, 2006.
3. B. Girod et al., "Mobile Visual Search," IEEE Signal Processing Magazine, vol. 28, no. 4, 2011, pp. 61-76.
4. D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, 2004, pp. 91-110.
5. H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded Up Robust Features," Proc. European Conf. Computer Vision (ECCV), Springer, 2006.
6. S.S. Tsai et al., "Mobile Product Recognition," Proc. ACM Multimedia, ACM Press, 2010.
7. V. Chandrasekhar et al., "Compressed Histogram of Gradients: A Low Bit Rate Feature Descriptor," Int'l J. Computer Vision, vol. 94, 2011, pp. 1-16.
8. H. Jegou, M. Douze, and C. Schmid, "Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search," Proc. European Conf. Computer Vision (ECCV), Springer, 2008.
9. J. Philbin et al., "Lost in Quantization—Improving Particular Object Retrieval in Large Scale Image Databases," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), IEEE Press, 2008.
10. O. Chum, J. Philbin, and A. Zisserman, "Near Duplicate Image Detection: Min-Hash and TF-IDF Weighting," Proc. British Machine Vision Conf. (BMVC), BMVA, 2008; .
11. X. Zhang et al., "Efficient Indexing for Large-Scale Visual Search," Proc. IEEE Int'l Conf. Computer Vision (ICCV), IEEE Press, 2009.
12. M.A. Fischler and R.C. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Comm. ACM, vol. 24, no. 6, 1981, pp. 381-395.
13. O. Chum, J. Matas, and J.V. Kittler, "Locally Optimized Ransac," Proc. German Assoc. Pattern Recognition Symp. (DAGM), Springer, 2003.
14. S.S. Tsai et al., "Fast Geometric Re-Ranking for Image Based Retrieval," Proc. IEEE Int'l Conf. Image Processing (ICIP), IEEE Press, 2010.
15. V. Chandrasekhar et al., "Low Latency Image Retrieval with Progressive Transmission of CHoG Descriptors," Proc. ACM Int'l Workshop Mobile Cloud Media Computing, ACM Press, 2010.
16. USNB Contribution: On Standardization of Mobile Visual Search, MPEG input document M17156, MPEG Requirements Group, Jan 2010.
17. Compact Descriptors for Visual Search: Call for Proposals, Applications, Scope and Objectives, Requirements, and Evaluation Framework, MPEG output documents N12038, N11529, N11530, N11531, N12039, MPEG Requirements Group, July 2010-Mar. 2011; http://mpeg.chiariglione.orgworking_ documents.htm .
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