COGE: A Novel Binary Feature Descriptor Exploring Anisotropy and Non-uniformity

  • Zhendong Mao
  • Yongdong Zhang
  • Qi Tian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8294)

Abstract

Matching keypoints across images is the base of numerous Computer Vision applications, which is often done with local feature descriptors. Hand-crafted descriptors such as SIFT and SURF are still established leaders in the field since they are discriminative as well as robust.

In this paper, we introduce a novel COGE descriptor, a simple yet effective method for keypoint description. By exploiting the anisotropy and the non-uniformity of the underlying gradient distributions, the proposed COGE is highly discriminative and robust. In addition, COGE contains only 480/240/120 bits and can be matched by using Hamming distance, making it ideal for mobile applications. To evaluate the performance of COGE, a comprehensive comparison against SIFT, SURF, ORB and BRISK is performed on three benchmark datasets: the dataset of Mikolajczyk, the INRIA Holidays and the UKbench. Experimental results show that our proposed COGE descriptor significantly outperforms existing schemes.

Keywords

Image Retrieval Query Image Cell Pair Orientation Component Binary Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Zhendong Mao
    • 1
  • Yongdong Zhang
    • 1
  • Qi Tian
    • 2
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Texas at San AntonioUSA

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