NDLI: Unsupervised image embedding using nonparametric statistics

Content Provider	IEEE Xplore Digital Library
Author	Guobiao Mei Shelton, C.R.
Copyright Year	2008
Description	Author affiliation: Univ. of California, Riverside, CA, USA (Guobiao Mei; Shelton, C.R.)
Abstract	Embedding images into a low dimensional space has a wide range of applications: visualization, clustering, and pre-processing for supervised learning. Traditional dimension reduction algorithms assume that the examples densely populate the manifold. Image databases tend to break this assumption, having isolated islands of similar images instead. In this work, we propose a novel approach that embeds images into a low dimensional Euclidean space, while preserving local image similarities based on their scale invariant feature transform (SIFT) vectors. We make no neighborhood assumptions in our embedding. Our algorithm can also embed the images in a discrete grid, useful for many visualization tasks. We demonstrate the algorithm on images with known categories and compare our accuracy favorably to those of competing algorithms.
Starting Page	1
Ending Page	4
File Size	376245
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761051
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistics Principal component analysis Image databases Data visualization Frequency Supervised learning Clustering algorithms Discrete transforms Application software Digital photography
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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