NDLI: Dimensionality reduction for image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Wu, P. Manjunath, B.S. Shin, H.D.
Copyright Year	2000
Description	Author affiliation: Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA (Wu, P.)
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.
Starting Page	726
Ending Page	729
File Size	449494
Page Count	4
File Format	PDF
ISBN	0780362977
ISSN	15224880
DOI	10.1109/ICIP.2000.899557
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-10
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image retrieval Nearest neighbor searches Multimedia databases Information retrieval Topology Principal component analysis Image databases Indexing Data structures Multidimensional systems
Content Type	Text
Resource Type	Article

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