Low-rate image retrieval with tree histogram coding

Authors:
Vijay Chandrasekhar;David M. Chen;Zhi Li;Gabriel Takacs;Sam S. Tsai;Radek Grzeszczuk;Bernd Girod
Affiliations:
Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Nokia Research Center, Palo Alto, CA;Stanford University
Venue:
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Year:
2009

Citing 9
Cited 1

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Recognition with a Vocabulary Tree

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Rate-efficient, real-time cd cover recognition on a camera-phone

MM '08 Proceedings of the 16th ACM international conference on Multimedia
Outdoors augmented reality on mobile phone using loxel-based visual feature organization

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Tree Histogram Coding for Mobile Image Matching

DCC '09 Proceedings of the 2009 Data Compression Conference
Location coding for mobile image retrieval

Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I

Heritage app: annotating images on mobile phones

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.