Graph-based classification of multiple observation sets

TL;DR

This paper introduces a graph-based classification method that leverages the low-dimensional manifold structure of multiple observations, such as images or video frames, to improve object recognition accuracy.

Contribution

It presents a novel low-complexity algorithm that exploits data manifold properties for classifying multiple observations, outperforming existing methods in face recognition.

Findings

Effective classification of multiple image sets demonstrated.

Superior performance in video-based face recognition.

Algorithm outperforms state-of-the-art solutions.

Abstract

We consider the problem of classification of an object given multiple observations that possibly include different transformations. The possible transformations of the object generally span a low-dimensional manifold in the original signal space. We propose to take advantage of this manifold structure for the effective classification of the object represented by the observation set. In particular, we design a low complexity solution that is able to exploit the properties of the data manifolds with a graph-based algorithm. Hence, we formulate the computation of the unknown label matrix as a smoothing process on the manifold under the constraint that all observations represent an object of one single class. It results into a discrete optimization problem, which can be solved by an efficient and low complexity algorithm. We demonstrate the performance of the proposed graph-based algorithm in the classification of sets of multiple images. Moreover, we show its high potential in video-based face recognition, where it outperforms state-of-the-art solutions that fall short of exploiting the manifold structure of the face image data sets.