Exploration of high-dimensional data manifolds for object classification

Nitesh Shah; Donald Waagen; Miguel Ordaz; Mary Cassabaum; Albert Coit

doi:10.1117/12.602500

19 May 2005 Exploration of high-dimensional data manifolds for object classification

Nitesh Shah, Donald Waagen, Miguel Ordaz, Mary Cassabaum, Albert Coit

Proceedings Volume 5807, Automatic Target Recognition XV; (2005) https://doi.org/10.1117/12.602500
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

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Nitesh Shah, Donald Waagen, Miguel Ordaz, Mary Cassabaum, and Albert Coit "Exploration of high-dimensional data manifolds for object classification", Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); https://doi.org/10.1117/12.602500

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KEYWORDS

Principal component analysis

Synthetic aperture radar

Feature extraction

Sensors

Associative arrays

Statistical analysis

Automatic target recognition

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