Dimensionality Reduction for Image Retrieval
 

P. Wu, B.S. Manjunath and H.D. Shin
Department of Electrical and Computer Engineering
University of California, Santa Barbara, CA 93106-9560
{peng, manj, hdshin}@iplab.ece.ucsb.edu
 

Abstract

Dimensionality reduction methods are of interest in
applications such as content based image and video
retrieval. In large multimedia databases, it may not be
practical to search through the entire database in order to
retrieve the nearest neighbors of a query. Good data structures
for similarity search and indexing are needed, and
the existing data structures do not scale well for the high
dimensional multimedia descriptors. We investigate the
use of weighted multi-dimensional scaling (WMDS) for
dimensionality reduction. The main objective of the
WMDS is to preserve the local topology of the high dimensional
space, i.e., to map the nearest neighbors in the high
dimensional space to nearest neighbors in the lower
dimensional space. In addition to the well known retrieval
accuracy as a measure of performance, we propose two
additional measures that take into account the ordinal
relationships among the nearest neighbors. Experimental
results are given.