Using non-negative matrix factorization toward finding an informative basis in spin-image data

Andrew J. Patterson; Nitesh N. Shah; Donald E. Waagen

doi:10.1117/12.776964

14 April 2008 Using non-negative matrix factorization toward finding an informative basis in spin-image data

Andrew J. Patterson, Nitesh N. Shah, Donald E. Waagen

Proceedings Volume 6967, Automatic Target Recognition XVIII; 696710 (2008) https://doi.org/10.1117/12.776964
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Three-dimensional (3D) Laser Detection and Ranging (LADAR) range data is being investigated for automatic target recognition applications. The spin-image provides a useful data representation for 3D point cloud data. In the spirit of recent work that shows ℓ₁-sparseness to be a useful data compression metric, we propose to use Nonnegative Matrix Factorization (NMF) to help find features that capture the salient information resident in the spin-image representation. NMF is a technique for decomposing nonnegative multivariate data into its 'parts', resulting in a compressed and usually sparse representation. As a surrogate for measured 3D LADAR data, we generate 3D point clouds from computer-aided-design models of two land targets, and we generate spin-images at multiple support scales. We select the support scale that provides the highest separability between the spin-image stacks from the two land targets. We then apply NMF to the spin-images at this support scale, and seek elements corresponding to meaningful parts of the land vehicles (e.g., a tank turret or truck wheels), that in a joint sense should provide significant discriminative capability. We measure the separability in the sparse NMF subspace. For measuring separability, we use the Henze-Penrose measure of multivariate distributional divergence.

Citation Download Citation

Andrew J. Patterson, Nitesh N. Shah, and Donald E. Waagen "Using non-negative matrix factorization toward finding an informative basis in spin-image data", Proc. SPIE 6967, Automatic Target Recognition XVIII, 696710 (14 April 2008); https://doi.org/10.1117/12.776964

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