NDLI: Similarity search using multi-space KL

Content Provider	IEEE Xplore Digital Library
Author	Cappelli, R. Maio, D. Maltoni, D.
Copyright Year	1999
Description	Author affiliation: Bologna Univ., Italy (Cappelli, R.)
Abstract	The Karhunen-Loeve transform is probably the most widely used statistical framework for dimensionality reduction in a broad range of scientific fields. Given a set of points in an n-dimensional space (the points can be derived from images, sounds, or other multimedia objects), KL provides a mapping which reduces the dimensionality of the input patterns to k (k/spl epsiv/3qn), without altering their structure too much. Unfortunately, KL suffers from some scalability problems: in fact, as the size of the database increases, the efficacy and efficiency of the transform progressively vanish. In this work we introduce the basics of a new generalization of KL (named MultiSpace KL or MKL) which allows the scalability problems to be solved and we show how MKL can be used for similarity searches in multimedia databases. The paper reports some preliminary experiments where MKL outperforms KL as the size of the database increases.
Starting Page	155
Ending Page	160
File Size	103199
Page Count	6
File Format	PDF
ISBN	0769502814
DOI	10.1109/DEXA.1999.795159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-09-03
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information retrieval Electrical capacitance tomography Scalability Read only memory Multimedia databases Feature extraction Tree data structures Principal component analysis Pattern recognition Image coding
Content Type	Text
Resource Type	Article

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