Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Efficient Content-Based Retrieval via Data Compression

Presented by: Dr. Ertem Tuncel

Abstract:

I will introduce a novel approach to content-based retrieval from very large and high dimensional (e.g., multimedia) databases. This approach is based on efficient compression of the feature vectors extracted from the objects in the database. The design procedure optimizes the query access time by jointly accounting for the database distribution and the query statistics. High compression ratios are achieved using appropriate vector quantization (VQ) techniques, namely, multi-stage VQ and split-VQ, which are especially suited for limited memory applications. The data set is first partitioned using the accumulated query history, and each partition of data points is separately compressed using a vector quantizer tailored to its distribution. The employed VQ techniques inherently provide a spectrum of points to choose from on the time/accuracy plane. This property is especially crucial for large multimedia databases where I/O time is a bottleneck, because it offers the flexibility to trade time for better accuracy. Our experiments demonstrate speedups of 20 to 35 over one of the most popular approximate search techniques.