ABSTRACT
Rapidly growing popularity of mobile devices has provided an important platform for image search. Compared with traditional large-scale image search, mobile image search is more sensitive to the computational complexity and transmission cost. Therefore, most of existing image search approaches are not optimal for mobile applications. How to improve the effectiveness and efficiency of mobile image search is still an open challenge nowadays. In this paper, we propose to build binary phrase which employs fast binary local descriptor and extracts spatial clues to improve both the efficiency and discriminative power of mobile search systems. We embed spatial information of binary phrases into inverted-index structure for flexible and efficient online verification. Large-scale experiments manifest that our algorithm achieves decent retrieval accuracy and efficiency.
- D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In Proc. CVPR, pages 2161--2168, 2006. Google ScholarDigital Library
- H. Jegou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In Proc. ECCV, 2008. Google ScholarDigital Library
- O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In Proc. ICCV, 2007.Google ScholarCross Ref
- J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In Proc. CVPR, 2007.Google ScholarCross Ref
- S. Zhang, Q. Tian, G. Hua, Q. Huang, and S. Li, Descriptive Visual Words and Visual Phrases for Image Applications. In Proc. ACM Multimedia, 2009. Google ScholarDigital Library
- W. Zhou, Y. Lu, H. Li, Y. Song, and Q. Tian. Spatial code for large scale partial-duplicate Web image search. In Proc. ACM Multimedia, 2010. Google ScholarDigital Library
- W. Zhou, H. Li, Y. Lu, Q. Tian, Large scale image search with geometric coding. In Proc. ACM Multimedia, 2011. Google ScholarDigital Library
- M. Calonder, V. Lepetit, C. Strecha, and P. Fua. Brief: Binary robust independent elementary features. Computer Vision--ECCV 2010, pages 778--792, 2010. Google ScholarDigital Library
- E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. Orb: an efficient alternative to sift or surf. 2011.Google Scholar
- D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91--110, 2004. Google ScholarDigital Library
- H. Bay, T. Tuytelaars, and L. Van Gool. Surf: Speeded up robust features. Computer Vision--ECCV 2006, pages 404--417, 2006. Google ScholarDigital Library
- S. Leutenegger, M. Chli, and R. Siegwart. Brisk: Binary robust invariant scalable keypoints. 2011.Google Scholar
- Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. Google ScholarDigital Library
- Z. Wu, Q. F. Ke, and J. Sun. Bundling features for large-scale partial-duplicate web image search. Proc. CVPR, 2009.Google Scholar
- L. Yang, P. Meer, and D. J. Foran. Multiple class segmentation using a unified framework over mean-shift patches. Proc. CVPR, pp. 1--8, 2007.Google ScholarCross Ref
- R. Ji, L.-Y. Duan, T. Huang, H. Yao, and W. Gao. Pkubench: A contextual rich benchmark for mobile visual search. In 95th MPEG Meeting. CDVS AD HOC Group Input Contribution, 2010.Google Scholar
- L. Fei-Fei, ImageNet: crowdsourcing, benchmarking & other cool things, CMU VASC Seminar, March, 2011.Google Scholar
Index Terms
- Scalable mobile search with binary phrase
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