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Volume 42 Issue 3
Mar.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(3): 497-505.
ZHENG Mingguo, WU Chengdong, CHEN Dongyue, et al. Local feature descriptor based on nonparametric marginal integration estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3): 497-505. doi: 10.13700/j.bh.1001-5965.2015.0156(in Chinese)
Citation: ZHENG Mingguo, WU Chengdong, CHEN Dongyue, et al. Local feature descriptor based on nonparametric marginal integration estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3): 497-505. doi: 10.13700/j.bh.1001-5965.2015.0156(in Chinese)
shu

Local feature descriptor based on nonparametric marginal integration estimation

doi: 10.13700/j.bh.1001-5965.2015.0156

  • College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Funds:  National Natural Science Foundation of China (61273078)
  • Received Date: 19 Mar 2015
  • Publish Date: 20 Mar 2016
    Abstract
  • A statistical model for the feature descriptor of local region was suggested to improve the image matching performance. This model is a marginal integration function model based on the gradient magnitude and orientation distribution. The marginal integration function on the discrete gradient orientations is the same as the magnitude accumulation orientation histogram of gradient vector field. Using the nonparametric estimator based on kernel function, we estimated this function and applied it to scale invariant feature transform (SIFT) descriptor. To enhance rotation invariance and distinctiveness and to reduce computational complexity for descriptor, local region around the feature point was selected as circle and partitioned to the 8 sub-regions by radial sampling grid. The marginal expectation functions are estimated in each sub-region and the feature vector consists of the function values on the 8 orientations for 8 sub-regions. Experiments show that this descriptor can improve detective rate (recall) for rotation and reduce computational complexity.

     

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