Distance Measure Based on an Embedding of the Manifold of K-Component Gaussian Mixture Models into the Manifold of Symmetric Positive Definite Matrices

TL;DR

This paper introduces a novel distance measure for Gaussian Mixture Models based on embedding into the manifold of symmetric positive definite matrices, enabling effective similarity assessment with demonstrated high accuracy on texture recognition benchmarks.

Contribution

It presents a new embedding of GMMs into SPD matrices, proves its mathematical properties, and applies it to measure GMM similarity with strong experimental results.

Findings

Achieved 98% accuracy on UIUC dataset

Achieved 92% accuracy on KTH-TIPS dataset

Achieved 93.33% accuracy on UMD dataset

Abstract

In this paper, a distance between the Gaussian Mixture Models(GMMs) is obtained based on an embedding of the K-component Gaussian Mixture Model into the manifold of the symmetric positive definite matrices. Proof of embedding of K-component GMMs into the manifold of symmetric positive definite matrices is given and shown that it is a submanifold. Then, proved that the manifold of GMMs with the pullback of induced metric is isometric to the submanifold with the induced metric. Through this embedding we obtain a general lower bound for the Fisher-Rao metric. This lower bound is a distance measure on the manifold of GMMs and we employ it for the similarity measure of GMMs. The effectiveness of this framework is demonstrated through an experiment on standard machine learning benchmarks, achieving accuracy of 98%, 92%, and 93.33% on the UIUC, KTH-TIPS, and UMD texture recognition datasets respectively.