A Novel Earth Mover’s Distance Methodology for Image Matching with Gaussian Mixture Models

View Researcher's Other Codes

MATLAB code for the paper: “A Novel Earth Mover’s Distance Methodology for Image Matching with Gaussian Mixture Models”.

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Peihua Li, Qilong Wang, and Lei Zhang
Journal/Conference Name 2013 International Conference on Computer Vision (ICCV 2013)
Paper Category
Paper Abstract The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content based image matching. Though the Earth Mover's Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We first present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more efficient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy- or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.
Date of publication 2013
Code Programming Language MATLAB

Copyright Researcher 2022