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Fast verification via statistical geometric for mobile visual search

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Abstract

In this paper, an efficient geometric statistics method is proposed to obtain the geometric information of the object, which can achieve fast visual re-ranking along with the localization of target-of-interest. Given an input pair of images, first we get a set of interest-point correspondences, and enumerate all potential pairs in each image, upon which we calculate the statistics of the corresponding pairs to yield the geometric similarity score. We use 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. Then fitting the statistics of geometric similarity scores into a Gaussian distribution that is used as a priori to determine the matching. The performance of our geometric scoring scheme is compared to the conventional geometric scoring schemes using orientation and scale. It is shown that our proposed statistically geometric method can generate fast geometric re-ranking. Meanwhile, we can accurately locate the target of search interest regardless of variations caused by occlusion and perspective changes.

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References

  1. Chen, D., Tsai, S., Chandrasekhar, V., Takacs, G., Singh, J., Girod, B.: Tree histogram coding for mobile image matching. In DCC (2009)

  2. Chandrasekhar, V., Takacs, G., Chen, D., Tsai, S., Grzeszczuk, R., Girod B.: Chog: compressed histogram of gradients a low bit-rate feature descriptor. In CVPR (2009)

  3. Chandrasekhar, V., Takacs, G., Chen, D., Tsai, S., Singh, J., Girod, B.: Transform coding of image feature descriptors. In VCIP (2009)

  4. Makar, M., Chang, C., Chen, D., Tsai, S., Girod, B.: Compression of image patches for local feature extraction. In ICASSP (2009)

  5. Girod, B., Chandrasekhar, V., Chen, D., Cheung, N.-M., Grzeszczuk, R., Reznik, Y., Takacs, G., Tsai, S., Vedantham, R.: Mobile visual search. In IEEE Signal Processing Magazine (2011)

  6. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In CVPR (2006)

  7. Ji, Rongrong, Yao, Hongxun, Liu, Wei, Sun, Xiaoshuai, Tian, Qi: Task dependent visual codebook compression. IEEE Trans. Image Process. TIP 21(4), 2282–2293 (2012)

    Article  MathSciNet  Google Scholar 

  8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    Article  Google Scholar 

  9. Xue, Y., Qian, X., Zhang, B.: Mobile image retrieval using multi-photo as query. In ICMEW (2013)

  10. Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In CVPR (2005)

  11. Jiang, H., Drew, M.S., Li, Z.-N.: Matching by linear programming and successive convexification. PAMI 29, 959–975 (2007)

    Article  Google Scholar 

  12. Jiang, H., Yu, S.X.: Linear solution to scale and rotation invariant object matching. In CVPR (2009)

  13. Li, H., Huang, J., Zhang, S., Huang, X.: Optimal object matching via convexification and composition. In ICCV (2011)

  14. Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: models and global optimization. In ECCV (2008)

  15. Li, H., Kim, E., Huang, X., He, L.: Object matching with a locally affine-invariant constraint. In CVPR (2010)

  16. Deng, C., Ji, R., Liu, W., Tao, D., Gao, X.: Visual reranking through weakly supervised multi-graph learning. In ICCV (2013)

  17. Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints In ICCV (2005)

  18. Cho, M., Lee, J., Lee, J.: Feature correspondence and deformable object matching via agglomerative correspondence clustering. In ICCV (2009)

  19. Cho, M., Lee, J., Lee, K.M.: Reweighted random walks for graph matching. In ECCV (2010)

  20. Cour, T., Srinivasan, P., Shi, J.: Balanced graph matching. In NIPS (2006)

  21. Chen, D., Tsai, S., Chandrasekhar, V., Takacs, G., Vedantham, R., Grzeszczuk, R., Girod, B.: Residual enhanced visual vector as a compact signature for mobile visual search. Sig. Process 93(8), 2316–2327 (2014)

    Article  Google Scholar 

  22. Liu, H., Yan, S.: Common visual pattern discovery via spatially coherent correspondences. In CVPR (2010)

  23. Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In CVPR (2008)

  24. Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In CVPR (2011)

  25. Philipin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabulary and fast spatial matching. In CVPR (2007)

  26. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27, 1615–1630 (2005)

    Article  Google Scholar 

  27. Guan, T., He, Y.F., Gao, J., Yang, J.Z., Yu, J.Q.: On-device mobile visual location recognition by integrating vision and inertial sensors. IEEE Trans. Multimed. 21(2), 32–41 (2014)

    Article  Google Scholar 

  28. Cho, M., Lee, K.M.: Progressive graph matching: making a move of graphs via probabilistic voting. In CVPR (2012)

  29. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In ECCV (2006)

  30. Ji, Rongrong, Duan, Ling-Yu., Chen, Jie, Huang, Tiejun, Gao, Wen: Mining compact 3D patterns for low bit rate mobile visual search. IEEE Trans. Image Process. TIP 23(7), 3099–3113 (2014)

    Article  MathSciNet  Google Scholar 

  31. Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In CVPR (2004)

  32. Crandall, D., Backstrom, L., Kleinberg, J.: Mapping the world’s photos. In WWW (2009)

  33. Mikolajczyk, K., Schmid, C.: Performance evaluation of local descriptors. In PAMI (2005)

  34. Chen, D., Tsai, S., Chandrasekhar, V.: Inverted index compression for scalable image matching. In DCC (2010)

  35. Deng, C., Ji, R., Tao, D., Gao, X., Li, X.: Weakly supervised multi-graph learning for robust image reranking. IEEE Trans. Multimed. TMM (2014)

  36. Irschara, A., Zach, C., Frahm, J. Bischof, H.: From structure-from-motion point clouds to fast location recognition. In CVPR (2009)

  37. Schindler, G., Brown, M.: City-scale location recognition. In CVPR (2007)

  38. Zhang, W., Kosecka, J.: Image based localization in urban environments. In 3DVT (2006)

  39. Lee, J.-A., Yow, K.-C. Sluzek, A.: Image based information guide on mobile devices. In: Advances in Visual Computing (2008)

  40. Hays, J., Efros, A.: Img2gps: estimating geographic information from a single image. In CVPR (2008)

  41. Zheng, Y., Zhao, M., Song, Y., Adam, H.: Tour the world: building a web-scale landmark recognition engine. In CVPR (2009)

  42. Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.-M.: Modeling and recognition of landmark image collections using iconic scene graphs. In ECCV (2008)

  43. Tsai, S., Chen, D., Takacs, G., Chandrasekhar, V., Singh, J., Girod, B.: Location coding for mobile image retrieval. In MobileMedia (2010)

  44. Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. PAMI 34(9), 1704–1716 (2012)

    Article  Google Scholar 

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Acknowledgments

This work is supported by the Nature Science Foundation of China (No. 61202143), the Natural Science Foundation of Fujian Province of China (Nos. 2013J05100, 2010J01345 and 2011J01367), the Fundamental Research Funds for the Central Universities (No. 2013121026 and 2011121052), the Xiamen University 985 project, the Research Fund for the Doctoral Program of Higher Education of China (No. 201101211120024), and the Special Fund for Developing Shenzhens Strategic Emerging Industries (No. JCYJ20120614164600201).

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Authors and Affiliations

  1. Cognitive Science Department, Xiamen University, Xiamen, 361005, Fujian, China

    Miaohui Zhang, Shaozi Li, Xianming Lin, Songzhi Su & Rongrong Ji

  2. Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, 361005, Fujian, China

    Miaohui Zhang, Shaozi Li, Xianming Lin, Songzhi Su & Rongrong Ji

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  1. Miaohui Zhang
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  2. Shaozi Li
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  3. Xianming Lin
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  4. Songzhi Su
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  5. Rongrong Ji
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Correspondence to Shaozi Li.

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Zhang, M., Li, S., Lin, X. et al. Fast verification via statistical geometric for mobile visual search. Multimedia Systems 22, 525–534 (2016). https://doi.org/10.1007/s00530-015-0452-y

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