Open AccessCompact and information loss‐bounded estimation of Gaussian mixture model for 3D spatial representation
Author(s) -
Kim J.W.,
Lee B.H.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2015.0677
Subject(s) - outlier , mixture model , bounded function , mathematics , gaussian , compact space , transformation (genetics) , representation (politics) , estimator , pattern recognition (psychology) , similarity (geometry) , invariant (physics) , consistency (knowledge bases) , algorithm , artificial intelligence , computer science , statistics , image (mathematics) , mathematical analysis , mathematical physics , biochemistry , physics , chemistry , quantum mechanics , politics , political science , law , gene
A new Gaussian mixture model (GMM) estimation technique is presented for three‐dimensional (3D) spatial representation. The GMM generated by the proposed technique is compact with bounded information loss as a result of using robust estimators and the Kullback‐Leibler divergence‐based Gaussian mixture reduction method. In addition, the proposed technique is not only robust to outliers, but quite close to invariant under similarity transformation. Experiments have demonstrate that the compactness and the consistency of the GMM are improved compared with existing 3D spatial representation models.