Sparse Graph-based Transduction for Image Classification

TL;DR

This paper introduces a novel image classifier called Sparse Graph-based Classifier (SGC) that combines Sparse Representation and Graph-based Transduction to improve robustness and discriminative power, especially with small or noisy datasets.

Contribution

The paper proposes SGC, a new classifier that integrates SR and GT using graph Laplacian, enhancing image classification performance over existing methods.

Findings

SGC outperforms state-of-the-art classifiers on four image datasets.

SGC is particularly effective with small training samples.

SGC demonstrates robustness in noisy sample conditions.

Abstract

Motivated by the remarkable successes of Graph-based Transduction (GT) and Sparse Representation (SR), we present a novel Classifier named Sparse Graph-based Classifier (SGC) for image classification. In SGC, SR is leveraged to measure the correlation (similarity) of each two samples and a graph is constructed for encoding these correlations. Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph. Finally, SGC can be obtained by plugging the graph Laplacian into the conventional GT framework. In the image classification procedure, SGC utilizes the correlations, which are encoded in the learned graph Laplacian, to infer the labels of unlabeled images. SGC inherits the merits of both GT and SR. Compared to SR, SGC improves the robustness and the discriminating power of GT. Compared to GT, SGC sufficiently exploits the whole data. Therefore it alleviates the undercomplete dictionary issue suffered by SR. Four popular image databases are employed for evaluation. The results demonstrate that SGC can achieve a promising performance in comparison with the state-of-the-art classifiers, particularly in the small training sample size case and the noisy sample case.