Rate-constrained distributed distance testing and its applications

  • Authors:

  • Chuohao Yeo;Parvez Ahammad;Hao Zhang;Kannan Ramchandran

  • Affiliations:

  • Dept. of EECS, University of California, Berkeley, 94720, USA;Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA;Dept. of EECS, University of California, Berkeley, 94720, USA;Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

We investigate a practical approach to solving one instantiation of a distributed hypothesis testing problem under severe rate constraints that shows up in a wide variety of applications such as camera calibration, biometric authentication and video hashing: given two distributed continuous-valued random sources, determine if they satisfy a certain Euclidean distance criterion. We show a way to convert the problem from continuous-valued to binary-valued using binarized random projections and obtain rate savings by applying a linear syndrome code. In finding visual correspondences, our approach uses just 49% of the rate of scalar quantization to achieve the same level of retrieval performance. To perform video hashing, our approach requires only a hash rate of 0.0142 bpp to identify corresponding groups of pictures correctly.