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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.
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