Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14692
Title: A novel earth mover's distance methodology for image matching with gaussian mixture models
Authors: Li, P
Wang, Q
Zhang, L 
Keywords: Gaussian Mixture Model (GMM)
Metric Learning for GMMs
Sparse Representation-based EMD (SR-EMD)
Issue Date: 2013
Publisher: IEEE
Source: 2013 IEEE International Conference on Computer Vision (ICCV), 1-8 December 2013, Sydney, NSW, p. 1689-1696 How to cite?
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.
URI: http://hdl.handle.net/10397/14692
ISBN: 
ISSN: 1550-5499
DOI: 10.1109/ICCV.2013.212
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