Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval

TL;DR

This paper introduces an ensemble metric learning method that efficiently learns sparse, low-rank Mahalanobis distances for high-dimensional data, significantly improving face verification and retrieval accuracy.

Contribution

It proposes a two-step ensemble approach combining sparse block diagonal and joint metric learning, enhancing scalability and accuracy in high-dimensional settings.

Findings

Outperforms state-of-the-art methods in face verification accuracy.

Maintains high computational efficiency with large-scale data.

Effectively handles high-dimensional feature spaces.

Abstract

Learning Mahanalobis distance metrics in a high- dimensional feature space is very difficult especially when structural sparsity and low rank are enforced to improve com- putational efficiency in testing phase. This paper addresses both aspects by an ensemble metric learning approach that consists of sparse block diagonal metric ensembling and join- t metric learning as two consecutive steps. The former step pursues a highly sparse block diagonal metric by selecting effective feature groups while the latter one further exploits correlations between selected feature groups to obtain an accurate and low rank metric. Our algorithm considers all pairwise or triplet constraints generated from training samples with explicit class labels, and possesses good scala- bility with respect to increasing feature dimensionality and growing data volumes. Its applications to face verification and retrieval outperform existing state-of-the-art methods in accuracy while retaining high efficiency.