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Proceedings Paper

Exploration of high-dimensional data manifolds for object classification
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Paper Abstract

This investigation discusses the challenge of target classification in terms of intrinsic dimensionality estimation and selection of appropriate feature manifolds with object-specific classifier optimization. The feature selection process will be developed via nonlinear characterization and extraction of the target-conditional manifolds derived from the training data. We investigate defining the feature space used for classification as a class-conditioned nonlinear embedding, i.e., each training and test image is embedded in a target-specific embedding and the resultant embeddings are used for statistical characterization. We compare and contrast this novel embedding technique with Principal Component Analysis. The α-Jensen Entropy Difference measure is used to quantify the object-conditioned separation between the target distributions in the feature spaces. We discuss and demonstrate the effect of feature space extraction on classification efficacy.

Paper Details

Date Published: 19 May 2005
PDF: 9 pages
Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); doi: 10.1117/12.602500
Show Author Affiliations
Nitesh Shah, Raytheon Co. Missile Systems (United States)
Donald Waagen, Raytheon Co. Missile Systems (United States)
Miguel Ordaz, Raytheon Co. Missile Systems (United States)
Mary Cassabaum, Raytheon Co. Missile Systems (United States)
Albert Coit, Raytheon Co. Missile Systems (United States)

Published in SPIE Proceedings Vol. 5807:
Automatic Target Recognition XV
Firooz A. Sadjadi, Editor(s)

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