3D object pose inference via kernel principal component analysis with image euclidian distance (IMED).

Abstract

Kernel Principal Component Analysis (KPCA) is a powerful non-linear unsupervised learning technique for high dimensional pattern analysis. KPCA on images, however, usually considers each image pixel as an independent dimension and does not take into account the spatial relationship of nearby pixels. In this paper, we show how the Image Euclidian Distance (IMED), which takes into account local pixel intensities, can efficiently be embedded into KPCA via the Kronecker product and Eigenvector projections, whilst still retaining desirable properties of Euclidian distance (such as kernel positive definitiveness and effective image de-noising). We demonstrate that KPCA with embedded IMED is a more intuitive and accurate technique than standard KPCA through a 3D object pose estimation application.