Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification

TL;DR

This paper introduces a scalable kernel-based framework using approximate infinite-dimensional covariance operators on Riemannian manifolds for improved image classification, combining theoretical advances with empirical validation.

Contribution

It proposes a finite-dimensional approximation of the Log-Hilbert-Schmidt distance enabling scalable kernel methods on infinite-dimensional covariance operators for image recognition.

Findings

Outperforms state-of-the-art methods on twelve datasets

Efficient approximation of the Log-HS distance for large datasets

Demonstrates effectiveness of covariance features in image classification

Abstract

This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds. Our formulation provides in particular a rich representation of image features by exploiting their non-linear correlations. Theoretically, we provide a finite-dimensional approximation of the Log-Hilbert-Schmidt (Log-HS) distance between covariance operators that is scalable to large datasets, while maintaining an effective discriminating capability. This allows us to efficiently approximate any continuous shift-invariant kernel defined using the Log-HS distance. At the same time, we prove that the Log-HS inner product between covariance operators is only approximable by its finite-dimensional counterpart in a very limited scenario. Consequently, kernels defined using the Log-HS inner product, such as polynomial kernels, are not scalable in the same way as shift-invariant kernels. Computationally, we apply the approximate Log-HS distance formulation to covariance operators of both handcrafted and convolutional features, exploiting both the expressiveness of these features and the power of the covariance representation. Empirically, we tested our framework on the task of image classification on twelve challenging datasets. In almost all cases, the results obtained outperform other state of the art methods, demonstrating the competitiveness and potential of our framework.