A continuous linear optimal transport approach for pattern analysis in image datasets.

ABSTRACT: We present a new approach to facilitate the application of the optimal transport metric to pattern recognition on image databases. The method is based on a linearized version of the optimal transport metric, which provides a linear embedding for the images. Hence, it enables shape and appearance modeling using linear geometric analysis techniques in the embedded space. In contrast to previous work, we use Monge's formulation of the optimal transport problem, which allows for reasonably fast computation of the linearized optimal transport embedding for large images. We demonstrate the application of the method to recover and visualize meaningful variations in a supervised-learning setting on several image datasets, including chromatin distribution in the nuclei of cells, galaxy morphologies

SUBMITTER: Kolouri S 

PROVIDER: S-EPMC4742369 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature