Research Article
Low-Rate Image Retrieval with Tree Histogram Coding
@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
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.