Compressed Histogram of Gradients: A Low-Bitrate Descriptor

  • Authors:

  • Vijay Chandrasekhar;Gabriel Takacs;David M. Chen;Sam S. Tsai;Yuriy Reznik;Radek Grzeszczuk;Bernd Girod

  • Affiliations:

  • Stanford University, Stanford, USA;Stanford University, Stanford, USA;Stanford University, Stanford, USA;Stanford University, Stanford, USA;Stanford University, Stanford, USA;Stanford University, Stanford, USA;Stanford University, Stanford, USA

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2012

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Abstract

Establishing visual correspondences is an essential component of many computer vision problems, which is often done with local feature-descriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile visual search applications. We propose a framework for computing low bit-rate feature descriptors with a 20脳 reduction in bit rate compared to state-of-the-art descriptors. The framework offers low complexity and has significant speed-up in the matching stage. We show how to efficiently compute distances between descriptors in the compressed domain eliminating the need for decoding. We perform a comprehensive performance comparison with SIFT, SURF, BRIEF, MPEG-7 image signatures and other low bit-rate descriptors and show that our proposed CHoG descriptor outperforms existing schemes significantly over a wide range of bitrates. We implement the descriptor in a mobile image retrieval system and for a database of 1 million CD, DVD and book covers, we achieve 96% retrieval accuracy using only 4 KB of data per query image.