Compression of image patches for local feature extraction

  • Authors:

  • Mina Makar;Chuo-Ling Chang;David Chen;Sam S. Tsai;Bernd Girod

  • Affiliations:

  • Information Systems Laboratory, Department of Electrical Engineering, Stanford University, USA;Information Systems Laboratory, Department of Electrical Engineering, Stanford University, USA;Information Systems Laboratory, Department of Electrical Engineering, Stanford University, USA;Information Systems Laboratory, Department of Electrical Engineering, Stanford University, USA;Information Systems Laboratory, Department of Electrical Engineering, Stanford University, USA

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

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Abstract

Local features are widely used for content-based image retrieval and object recognition. We present an efficient method for encoding digital images suitable for local feature extraction. First, we find the patches in the image corresponding to the detected features. Then, we extract these patches at their characteristic scale and orientation and encode them for efficient transmission. A Discrete Cosine Transform (DCT) with adaptive block size is used for patch compression. We compare this method to directly compressing feature descriptors using transform coding. Experimental results show the superior performance of our technique. Image patches can be compressed to rates around 55 bits/patch (18x compression relative to uncompressed SIFT feature descriptors) and still achieve good image matching performance.