A feature point clustering algorithm based on GG-RNN

  • Authors:

  • Zhiheng Zhou;Dongkai Shen;Lei Kang;Jie Wang

  • Affiliations:

  • College of Electronics & Information Engineering, South China University of Technology, Guangzhou, China,Desay Corporation, Huizhou, China;College of Electronics & Information Engineering, South China University of Technology, Guangzhou, China;College of Electronics & Information Engineering, South China University of Technology, Guangzhou, China;Institute of Acoustics and Lighting Technology, Guangzhou University, Guangzhou, China

  • Venue:
  • ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2013

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Abstract

In the field of object recognition in computer vision, feature point clustering algorithm has become an important part of the object recognition. After getting the object feature points, we make the feature points in clustering in the use of GG-RNN clustering algorithm, to achieve multi-part of the object clustering or the multi-object clustering. And the GG-RNN clustering algorithm we propose innovatively, is merged with the grayscale and gradient information based on Euclidean distance in the similarity calculation. Compared with the distance description of basic RNN algorithm, the similarity calculation of high-dimensional description of GG-RNN will improve the accuracy of the clustering in different conditions.