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5th International Mobile Multimedia Communications Conference

Research Article

Low-Rate Image Retrieval with Tree Histogram Coding

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  • @INPROCEEDINGS{10.4108/ICST.MOBIMEDIA2009.7496,
        author={Vijay Chandrasekha and David M. Chen and Zhi Li and Gabriel Takacs and Sam S. Tsai and Radek Grzeszczuk and Bernd Girod},
        title={Low-Rate Image Retrieval with Tree Histogram Coding},
        proceedings={5th International Mobile Multimedia Communications Conference},
        publisher={ICST},
        proceedings_a={MOBIMEDIA},
        year={2010},
        month={5},
        keywords={},
        doi={10.4108/ICST.MOBIMEDIA2009.7496}
    }
    
  • Vijay Chandrasekha
    David M. Chen
    Zhi Li
    Gabriel Takacs
    Sam S. Tsai
    Radek Grzeszczuk
    Bernd Girod
    Year: 2010
    Low-Rate Image Retrieval with Tree Histogram Coding
    MOBIMEDIA
    ICST
    DOI: 10.4108/ICST.MOBIMEDIA2009.7496
Vijay Chandrasekha1,*, David M. Chen1,*, Zhi Li1,*, Gabriel Takacs1,*, Sam S. Tsai1,*, Radek Grzeszczuk2,*, Bernd Girod1,*
  • 1: Information Systems, Lab Stanford University.
  • 2: Nokia Research Center, Palo Alto, CA.
*Contact email: vijayc@stanford.edu, dmchen@stanford.edu, leeoz@stanford.edu, gtakacs@stanford.edu, sstsai@stanford.edu, radek.grzeszczuk@nokia.com, bgirod@stanford.edu

Abstract

To perform image retrieval using a mobile device equipped with a camera, the mobile captures an image, transmits data wirelessly to a server, and the server replies with the associated database image information. Query data compression is crucial for low-latency retrieval over a wireless network. For fast retrieval from large databases, Scalable Vocabulary Trees (SVT) are commonly employed. In this work, we propose using distributed image matching where corresponding Tree-Structured Vector Quantizers (TSVQ) are stored on both the mobile device and the server. By quantizing feature descriptors using an optimally pruned TSVQ on the mobile device and transmitting just a tree histogram, we achieve very low bitrates without sacrificing recognition accuracy. We carry out tree pruning optimally using the BFOS algorithm and design criteria for trading off classification-error-rate and bitrate effectively. For the well known ZuBuD database, we achieve 96% accuracy with only »1000 bits per image. By extending accurate image recognition to such extremely low bitrates, we can open the door to new applications on mobile networked devices.

Published
2010-05-16
Publisher
ICST
Modified
2010-05-16
http://dx.doi.org/10.4108/ICST.MOBIMEDIA2009.7496
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