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Multimedia Systems

, Volume 22, Issue 4, pp 525–534 | Cite as

Fast verification via statistical geometric for mobile visual search

  • Miaohui Zhang
  • Shaozi Li
  • Xianming Lin
  • Songzhi Su
  • Rongrong Ji
Special Issue Paper
  • 301 Downloads

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.

Keywords

Mobile visual search Statistics geometric Object location BoW 

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Miaohui Zhang
    • 1
    • 2
  • Shaozi Li
    • 1
    • 2
  • Xianming Lin
    • 1
    • 2
  • Songzhi Su
    • 1
    • 2
  • Rongrong Ji
    • 1
    • 2
  1. 1.Cognitive Science DepartmentXiamen UniversityXiamenChina
  2. 2.Fujian Key Laboratory of the Brain-like Intelligent SystemsXiamen UniversityXiamenChina

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